• Research article
  • Open access
  • Published: 01 May 2020

A systematic literature review of existing conceptualisation and measurement of mental health literacy in adolescent research: current challenges and inconsistencies

  • Rosie Mansfield   ORCID: orcid.org/0000-0002-8703-5606 1 ,
  • Praveetha Patalay 2 &
  • Neil Humphrey 1  

BMC Public Health volume  20 , Article number:  607 ( 2020 ) Cite this article

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With an increased political interest in school-based mental health education, the dominant understanding and measurement of mental health literacy (MHL) in adolescent research should be critically appraised. This systematic literature review aimed to investigate the conceptualisation and measurement of MHL in adolescent research and the extent of methodological homogeneity in the field for meta-analyses.

Databases (PsycINFO, EMBASE, MEDLINE, ASSIA and ERIC) and grey literature were searched (1997–2017). Included articles used the term ‘mental health literacy’ and presented self-report data for at least one MHL domain with an adolescent sample (10–19 years). Definitions, methodological and contextual data were extracted and synthesised.

Ninety-one articles were identified. There was evidence of conceptual confusion, methodological inconsistency and a lack of measures developed and psychometrically tested with adolescents. The most commonly assessed domains were mental illness stigma and help-seeking beliefs; however, frequency of assessment varied by definition usage and study design. Recognition and knowledge of mental illnesses were assessed more frequently than help-seeking knowledge. A mental-ill health approach continues to dominate the field, with few articles assessing knowledge of mental health promotion.

Conclusions

MHL research with adolescent samples is increasing. Results suggest that a better understanding of what MHL means for this population is needed in order to develop reliable, valid and feasible adolescent measures, and explore mechanisms for change in improving adolescent mental health. We recommend a move away from ‘mental disorder literacy’ and towards critical ‘mental health literacy’. Future MHL research should apply integrated, culturally sensitive models of health literacy that account for life stage and acknowledge the interaction between individuals’ ability and social and contextual demands.

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Around 50% of mental health difficulties have their first onset by age 15 [ 1 , 2 ] and are associated with negative outcomes such as lower educational attainment and physical health problems [ 3 ]. Approximately 10–20% of young people are affected worldwide, and many more will experience impairing mental distress at varying degrees across the mental health continuum [ 4 , 5 , 6 , 7 , 8 ]. Adolescence is a critical period of transition, characterised by physical, cognitive, emotional, social and behavioural development [ 9 ]. It has therefore been identified as a particularly important developmental phase for improving ‘mental health literacy’ (MHL) and promoting access to mental health services [ 10 , 11 ]. However, better understanding of the conceptualisation and measurement of MHL in this population is needed.

MHL was first defined as ‘ knowledge and beliefs about mental disorders which aid their recognition, management or prevention’ ( [ 12 ] pp 182) and consisted of six domains: ‘1) the ability to recognise specific disorders or different types of psychological distress; 2) knowledge and beliefs about risk factors and causes; 3) knowledge and beliefs about self-help interventions; 4) knowledge and beliefs about professional help available; 5) attitudes which facilitate recognition and appropriate help-seeking, and 6) knowledge of how to seek mental health information’ ( [ 13 ] pp 396). Domains were later revised to include early recognition, prevention and mental health first aid skills [ 14 ]. The most recent definition comprises four broad domains aligned with current definitions of health literacy: ‘1) understanding how to obtain and maintain positive mental health; 2) understanding mental disorders and their treatments; 3) decreasing stigma related to mental disorders, and 4) enhancing help-seeking efficacy (knowing when and where to seek help and developing competencies designed to improve one’s mental health care and self-management capabilities’ ( [ 15 ] pp 155).

In a review of MHL measurement tools, O’Connor et al. revealed that the most commonly assessed domain was recognition of mental disorders. No studies assessed either knowledge of how to seek information or knowledge of self-help interventions [ 16 ]. The focus on recognition of mental disorders, along with knowledge about risk factors, causes and appropriate treatments, has been criticised for promoting the psychiatric and biogenetic conceptualisation of mental illness [ 17 , 18 ]. Despite being found to reduce blame, biogenetic explanations and attributions can lead to misconceptions about dangerousness and unpredictability and pessimism about recovery [ 19 ]. Early research also suggested that biogenetic causal theories increase a desire for social distance [ 20 , 21 ]. MHL modelled on recognition of psychiatric labels, and diagnostic language such as ‘disorder’, often leads to psychosocial predictors being ignored, and more negative attitudes towards individuals experiencing mental distress [ 22 , 23 ].

These criticisms, in line with broader socio-cultural approaches to literacy [ 24 ] understand MHL as a socio-political practice used to communicate, and make dominant, the psychiatric discourse. This appears to undermine attempts to reduce stigma, the most common outcome of school-based MHL interventions [ 25 ]. In their review of MHL measurement tools, O’Connor et al. excluded all disorder specific scales, claiming that ‘ MHL by definition should encompass knowledge and attitudes relating to a range of mental health disorders and concepts .’ ( [ 16 ] pp 199). Chambers et al. further criticised current MHL definitions for being narrow in focus with a predominantly mental-ill health approach, ignoring the complete mental health state that goes beyond the dichotomy of illness and wellness [ 26 , 27 ]. The difference between literacy about mental disorders and the ability to seek out, comprehend, appraise and apply information relating to the complete mental health state is an emerging point of discussion, and has seen MHL re-defined to include self-acquired knowledge and skills relating to positive psychology [ 28 , 29 ]. This aligns with the World Health Organisation’s (WHO) definition of mental health, which includes subjective wellbeing, optimal functioning and coping, and recognises mental health beyond the absence of disorder [ 30 ].

In response to increasingly inclusive definitions of MHL, Spiker and Hammer presented the argument for MHL as a ‘multi-construct theory, rather than a multi-dimensional construct’ ( [ 31 ] pp 3). The proposal suggested that by stretching the MHL construct, researchers have reduced the consistent use of the definition across studies, resulting in heterogeneous measurement [ 32 ]. Reviews of the psychometric properties of MHL measurement tools support this argument, and conclude that more consistent measurement with valid scales is needed [ 33 , 34 , 35 , 36 ]. Spiker and Hammer also outline problems with construct irrelevant variance [ 31 ], in which measures capture more than they intended to. Furthermore, they note that construct proliferation or the ‘jingle jangle fallacy’ [ 37 ], in which scales may have different labels but measure the same construct, and vice versa, increase problems with discriminant validity. Understanding MHL as a multi-construct theory could help delineate between its broad domains: recognition, knowledge, stigma and help-seeking beliefs, and acknowledge their complexity.

Internationally, there is growing political interest in child and adolescent mental health promotion and education [ 6 , 38 ]. Despite limited evidence, it is suggested that educating the public by improving their ability to recognise mental disorders, and increasing help-seeking knowledge, can promote population mental health [ 39 , 40 ]. Furthermore, a reduction in stigmatising attitudes is consistently reported to improve help-seeking [ 41 , 42 ]. MHL, by definition, includes these interacting domains. However, despite a comprehensive set of reviews that assess the psychometric properties of MHL measurement tools [ 33 , 34 , 35 , 36 ], there is no systematic literature review, to date, that assesses the current conceptualisation and measurement of MHL across adolescent research. Being able to clearly operationalise what is meant by a MHL intervention and meta-analyse their effectiveness, will have implications for the investment in school and population level initiatives. Similarly, being able to conduct time trend analyses that plot possible improvements in adolescents’ MHL against mental health outcomes, will reveal the extent to which population level improvements in MHL promote mental health. First though, we must have a clear picture of the understanding of MHL in adolescent research and how it is currently being measured.

Objectives and research questions

The aim of the current study was therefore to examine the ways in which MHL has been conceptualised and measured in adolescent research to date, and explore the extent of methodological homogeneity in the field for meta-analyses. We set out to answer the following research questions: 1) What are the most common study designs, contexts, and aims? 2) How is MHL conceptualised? 3) What are the most commonly measured domains of MHL, and do these vary by study design and definition usage? 4) To what extent do articles use measures that have evidence of validity for use with adolescent samples? 5) Is there enough methodological homogeneity in the field to conduct meta-analyses?

A protocol was published on PROSPERO in December 2017 (reference: CRD42017082021 ), and was updated periodically to reflect the progress of the review. Relevant PRISMA guidelines for reporting were followed [ 43 ].

Eligibility criteria

Articles were included with adolescent samples aged between 10 and 19 [ 44 ]. Samples with a mean age outside of this range were excluded. If no mean was presented and the age range fell outside of the criterion, articles were only included if results were presented for sub-groups (e.g. 12–17 years from a sample aged 12–25). General MHL and diagnosis-specific literacy research was included. Articles with quantitative study designs and extractable self-report data for at least one time point measurement of any MHL domain were eligible. These criteria ensured that only articles with extractable data from adolescents, who had not yet received any form of intervention were included. At the full text screening phase, articles published before 1997, based on the date of the first MHL definition [ 12 ], and those that did not explicitly use the term ‘mental health literacy’ or a diagnosis-specific equivalent (e.g. ‘depression literacy’) were excluded. By applying this criterion, the current study was able to present the number of articles that measured domains without referring to MHL. Identifying cases where researchers measure the same construct but use different labels is important when considering conceptualisation and meta-analyses.

Only articles available in English were included. Specific populations such as clinical/patient populations and juvenile offenders were excluded, as were university students. In contrast to schools in most countries, universities are not universal, with only a sub-set of young people entering higher education. University samples were therefore not seen as representative and often included participants outside the age criterion. Post-partum and later life neurocognitive disorders (e.g. Alzheimer’s disease) were removed given their limited relevance for this age group. In line with other MHL reviews [ 33 ], articles with a focus on substance abuse were excluded to avoid reviewing a large number of adolescent risk behaviour studies and substance abuse prevention programmes.

Search strategy

The search strategy was developed to include a number of combinations of terms to ensure that literature relating to different domains of MHL were captured. Population terms such as ‘adolescen*’ or ‘young people*’ had to be present and mental health related terms (e.g. ‘mental health’ and ‘mental disorders’) were exploded to capture general MHL and diagnosis-specific studies. Similarly, outcome terms (e.g. ‘health literacy’ and ‘health education’) were exploded, and domain specific terms included (e.g. ‘knowledge’, ‘recogni*’, ‘attitud*’, ‘stigma*’, ‘help-seek*’, ‘prevent*’ or ‘positive*’). See Additional File 1 . for an example search strategy.

Data sources

The following databases were searched from their start date to the search dates (November 2017): PsycINFO, EMBASE, MEDLINE, ASSIA, and ERIC. Key authors were also contacted to identify grey literature. References were harvested from related reviews and all papers identified in the search. Hand searches of key authors’ publication lists were also conducted, and Google Scholar was used to find studies known by the authors but not identified in the database searches.

Article selection

Results from the database searches were saved to Endnote and duplicates were removed. The lead author screened the article titles and abstracts to identify those that met the inclusion criteria. Full texts were then screened and reasons for exclusion were recorded. Any uncertainties were resolved through discussion with other members of the research team. A sub-set of 20 articles were screened at full text stage by the third author, and a strong level of agreement was found (k = .78, p  = .001).

Data extraction

Research was assessed on an article level (rather than by study) for the purposes of investigating the conceptualisation of MHL. The fact that authors break MHL down into component parts to write separate articles is support for identifying which domains are more commonly associated with the use of the term. Data on the following methodological factors were extracted from eligible articles using a uniform data extraction form: year of publication, country and setting (community (research conducted outside of the school setting e.g. population level surveys) vs. school-based research), study design (intervention vs. population-based), primary aims, MHL definition and use of the term, general MHL vs. diagnosis-specific literacy, number/types of MHL domains measured, and measurement tools (e.g. vignette, yes/no, Likert scales).

Data analysis

A content analysis was conducted using NVivo 12 to organise articles by their primary aim and understand the conceptualisation of MHL based on the definition presented and use of the term. Frequencies and percentages for each group were calculated and articles coded based on whether they included items related to general MHL or diagnosis-specific literacy. Existing definitions of MHL [ 12 , 13 , 14 , 15 , 28 ] were used to create a coding framework that clearly delineated its broad constituent domains (e.g. recognition, knowledge, stigma and beliefs), the object of these domains (e.g. mental illnesses, mental health prevention and promotion, and help-seeking), and their directionality (e.g. self vs. other) – see Fig.  1 .

figure 1

MHL Coding Framework

Mental illness stigma was assessed using existing conceptualisation i.e. personal and perceived stigma relating to self (intra-personal) and others (inter-personal), and broad domains (e.g. attitudes and beliefs, emotional reactions, and social distancing) [ 45 ]. The coding of help-seeking beliefs was informed by the theory of planned behaviour [ 46 ], assessing not only help-seeking intentions but also help-seeking confidence and self-perceived help-seeking knowledge, perceived helpfulness of referrals, help-sources and treatments, help-seeking stigma and perceived help-seeking barriers. A distinction was also made between help-seeking beliefs for self (intra-personal) vs. others (inter-personal). Although not explicitly included in any MHL definition, help-seeking behaviour was also assessed as the term is sometimes confused with help-seeking intentions. Domains were coded at an item level due to many articles presenting this form of data (e.g. % of sample that answered each item correctly as opposed to a scale mean). Frequencies and percentages were produced across all articles and by study design and definition usage.

Assessment of measures

An assessment of all MHL related measurement tools was conducted in order to assess methodological homogeneity across articles, and whether there was evidence that the measures were psychometrically valid for adolescent samples. In order to present instruments with the most comprehensive psychometric assessments, measures were coded based on whether an article existed with the primary aim of establishing its psychometric properties with an adolescent sample.

Article selection and characteristics

In total, 206 articles were identified that presented extractable adolescent data on at least one MHL domain. Of these, 91 articles (44%) used the term ‘mental health literacy’. Those that did not use the term ( N  = 115, 56%), were therefore not perceived to have intended to explicitly measure the construct and were not included beyond this point. (see Fig.  2 . for a PRISMA flowchart of articles, Additional File 2 . for the full set of coded articles, and Additional File 3 . for the reference list of included articles).

figure 2

PRISMA Flowchart of Included Studies

Synthesised findings

Design, context and aims.

Figure  3 shows the number of publications by year and country. Australian research dominated the field up until 2013, at which point there was an increase in research being published globally. Australia (34%), USA (15%), Canada (9%), Republic of Ireland (9%) and the UK (8%) have published the majority of research between 2003 and 2017.

figure 3

Publication Count by Year and Country

Table  1 presents a summary of articles’ study design, context and primary aim. The majority of articles reported on school-based studies. Articles with the primary aim of describing levels of MHL also included variables such as age, school year, gender, education, socio-economic variables, occupation, urbanicity, mental health status and previous mental health service use.

  • Conceptualisation

Of the 91 articles that used the term ‘mental health literacy’, only 41 (45%) defined it. The most common definition, presented by 29 out of 41 (71%) articles, was that coined by Jorm and colleagues [ 12 ]. A further 3 articles (7%) used a simplified or adapted version of this definition [ 47 , 48 , 49 ]. Four articles (10%) defined MHL as related to knowledge only (e.g. ‘knowledge of mental health problems as well as the sources of help available’ ; ( [ 50 ] pp. 485) . The full list of MHL domains presented by Jorm and colleagues [ 13 ], was included in over a third ( N  = 14, 34%) of articles that defined the term. However, there was some variation. For example, very few of these articles ( N  = 2, 14%) referred to different types of psychological distress as well as mental disorders when presenting the recognition domain. Furthermore, in most cases ( N  = 11, 79%), ‘knowledge and beliefs’ was replaced with ‘knowledge’ only, for domains relating to causes and risk factors, self-help strategies and professional help available.

A small number of articles that defined MHL ( N  = 5, 12%) presented Jorm’s additional domains relating to mental health first aid skills and advocacy [ 14 ]. Some articles ( N  = 4, 10%) provided examples of specific MHL domains, namely recognition of mental disorders and knowledge and beliefs about appropriate help-seeking and treatment, as opposed to presenting a comprehensive list. An emerging group of articles ( N  = 5, 12%) either acknowledged mental health promotion as a component of MHL or presented Kutcher and colleagues’ four broad domains including ‘understanding how to obtain and maintain good mental health’ ( [ 15 ] pp 155).

Regardless of whether a definition was provided, approximately one third of identified articles ( N  = 31, 34%) referred to MHL as a construct separate to mental illness stigma, with some suggesting that MHL predicts stigma. For example, articles described the measurement of these constructs as separate (e.g. ‘All respondents were then asked a series of questions that assessed sociodemographic characteristics, mental health literacy, stigma …’; ([ 51 ] pp. 941), and referred to or presented a relationship between the two constructs (e.g. ‘Participants with higher MHL displayed more negative attitudes to mental illness’ ; ( [ 52 ] pp. 100) . There were also instances where articles presented MHL as a predictor of help-seeking intentions and attitudes (e.g. ‘Studies indicate that in general, mental health literacy improves help seeking attitudes’ ; [ 53 ] (pp. 2), or used the term MHL to refer only to improved knowledge (e.g. ‘to assess the extent to which the students had learned the curriculum and developed what we called ‘depression literacy’ ; ([ 54 ] pp. 230).

  • Measurement

Thirty-nine (43%) articles included items relating to general MHL. The exact terminology varied across studies e.g. mental disorder [ 55 ], mental illness [ 56 ], mental health problem [ 57 ], and mental health issue [ 58 ]. Few articles included items relating to mental health as opposed to mental ill-health. Bjørnsen et al. developed and validated a scale to assess adolescents' knowledge of how to obtain and maintain good mental health [ 28 ]. Kutcher et al. and McLuckie et al. also included an individual knowledge item that assessed an understanding of the complete mental health state (e.g. ‘People who have mental illness can at the same time have mental health’ ) [ 59 , 60 ].

Table  2 . presents the frequency and percentage of articles that assessed different types of diagnosis-specific literacy. In line with this focus, 57 (63%) articles utilized a vignette methodology, basing questions on descriptions, stories and scenarios relating to an individual meeting diagnostic criteria for a given mental disorder. Of these articles, 12 (21%) used comparator vignettes describing individuals with physical health problems (e.g. asthma or diabetes), control characters with good academic attainment, or ‘normal issues’ or mental health problems relating to stressful life events (e.g. the death of an elderly relative or the end of a romantic relationship). Table  3 . presents the frequency and percentage of articles that assessed different domains of MHL.

Measurement tools were too heterogeneous to conduct meta-analyses. As noted in Table 1 , four articles (4%) had the primary aim of validating MHL related measures with adolescent samples [ 28 , 55 , 61 , 62 ]. The scales assessed in Bjørnsen et al. and Pang et al. measured only one broad domain of MHL; knowledge of mental health promotion and mental illness stigma respectively [ 28 , 62 ]. Hart et al. assessed the psychometric properties of a depression knowledge questionnaire and found a one factor general knowledge latent structure to be the best fit to the data [ 61 ]. Campos et al. aimed to provide a more comprehensive assessment of MHL, and by psychometrically assessing a pool of items, developed a 33-item tool with three latent factors: first aid skills and help seeking, knowledge/stereotypes, and self-help strategies [ 55 ]. A further 22 articles (24%), stated that some items or scales had been developed for the purpose of the study.

Thirty-nine articles (43%) stated that they based their items on Jorm and colleagues original MHL survey or later 2006 and 2011 versions [ 12 , 63 ]. Furthermore, two articles (2%) included items from the Mental Health First Aid Questionnaire (MHFAQ) as detailed by Hart et al. [ 64 ]. However, there is no evidence of the validity of these surveys as whole scales, and researchers commonly selected and modified items. The Friend in Need Questionnaire, similar to Jorm and colleagues MHL survey in that it covers multiple MHL domains, was developed by Burns and Rapee to avoid leading multiple-choice answers. Instead, open-ended responses were coded in order to quantify levels of MHL [ 65 ]. Despite finding six articles (7%) that utilised a version of this questionnaire, no published validation paper was found. As part of the Adolescent Depression Awareness Programme (ADAP), an Adolescent Depression Knowledge Questionnaire (ADKQ) was developed and later validated [ 61 ]. Six articles (7%), including the validation paper, presented data using versions of the ADKQ.

Due to the multi-faceted nature of stigma, a range of measurement tools were identified across articles. The Attribution Questionnaire (AQ-27) was originally developed by Corrigan and colleagues [ 66 , 67 ] along with a brief 9-item scale (r-AQ) covering the following emotional reactions: blame, anger, pity, help, dangerousness, fear, avoidance, segregation and coercion. A similar 8-item version (AQ-8-C) was also developed for children [ 68 ]. The r-AQ was adapted by Watson et al. for use with middle school aged adolescents [ 69 ], and a 5-item version was more recently validated by Pinto et al. [ 70 ]. Four articles (4%) identified in this review used variations of the r-AQ.

Link et al. developed the 5-item Social Distance Scale (SDS) [ 71 ], which was later adapted for young people [ 72 ]. This version was more recently validated with a large sample aged 15–25 [ 73 ]. Five articles (5%) cited this version of the SDS. Seven articles (8%) used variations of the World Psychiatric Association’s (WPA) social distance items [ 74 ]; however, no adolescent validation paper was found. This review also found factual and attitudinal WPA scales presented by Pinfold et al. including the Myths and Facts About Schizophrenia Questionnaire. In total, these scales, or modified versions, were used in eight articles (9%), but no validation papers were found. The Reported and Intended Behaviour Scale (RIBS) [ 75 ] was utilised in three articles (3%). This scale has been translated into Japanese and Italian, and there is evidence of its validity with adult and university student samples [ 76 , 77 ]. The evidence of its validity with an adolescent sample was mixed [ 78 ].

The Depression Stigma Scale (DSS) was developed by Griffiths et al. to measure personal and perceived depression stigma [ 79 ]. Yap et al. later validated the DSS and confirmed that personal and perceived stigma were distinct constructs comprised of ‘weak-not-sick’ and ‘dangerous/unpredictable’ factors in a sample aged 15–25 [ 73 ]. Six articles (7%) utilised a version of the DSS, more commonly the items relating to personal stigma. Items from the Opinions about Mental Illness Scale (OMI) were used in two articles (2%). The original scale was cited by both [ 80 ], however, a Chinese version of the OMI has been tested for validity with a sample of secondary school students [ 81 ]. Other validated stigma scales identified included: the Attitudes Toward Serious Mental Illness Scale–Adolescent Version (ATSMI-AV) [ 82 ] ( N  = 1, 1%) and the Subjective Social Status Loss Scale [ 83 ] ( N  = 1, 1%). Measures of help-seeking attitudes and intentions were often not validated with adolescent samples. Two articles (2%) modified the General Help Seeking Questionnaire (GHSQ), previously validated for use with high school students [ 84 ]. A further two articles (2%) utilised the Self-Stigma of Seeking Help (SSOSH) scale; however, tests of its validity have only been conducted with college students [ 85 ].

The aims of this review were to investigate the conceptualisation and measurement of MHL in adolescent research, and scope the extent of methodological homogeneity for possible meta-analyses. The review clearly shows an increase in school-based MHL research with adolescent samples in recent years. This makes sense given that adolescence is increasingly identified as an important period for improving MHL and access to mental health services [ 6 , 10 , 11 , 38 ]. However, the field is still dominated by research from Western, developed countries and takes a predominantly mental-ill health approach. Furthermore, numerous challenges and inconsistencies have emerged in the field over the past 20 years.

Included articles were required to use the term ‘mental health literacy’ or a diagnosis-specific equivalent. However, by first including all articles that presented data for at least one MHL domain, a large number of articles that measured domains without referring to MHL were revealed. Researchers were measuring the same constructs but providing different labels indicating problems with discriminant validity [ 31 , 37 ]. It must be acknowledged that some of the articles included in the final set may have used the term without intending to measure the whole construct, and some articles were removed that measured multiple domains. For example, 16 intervention studies, previously included in a systematic literature review of the effectiveness of MHL interventions [ 25 ], were excluded from this current review because they did not use the term. Despite the exclusion of some potentially relevant data on a domain level, this criterion was considered most appropriate given one of the aims was to assess the conceptualisation of MHL.

Although under half of the articles identified defined MHL, those that did predominantly used definitions from Jorm and colleagues [ 12 , 13 , 14 ]. However, the various adaptations and interpretations of the original definition has clearly led to a lack of construct travelling in the field, in particular, confusion about the inclusion of beliefs and stigma related constructs as MHL domains. Furthermore, few articles referred to mental health and varying degrees of psychological distress in addition to mental illness, supporting the argument that current MHL definitions take a predominantly mental-ill health approach [ 16 , 26 ].

Although an adolescent specific definition of MHL may not be necessary, definitions frequently adopted by articles in this review were developed for adults. It is important for future research to consider not only cognitive development but also the unique social structures and vulnerabilities of adolescents in the conceptualisation and assessment of MHL. Given that the definition of adolescence in the current study ranges from 10 to 19 years, it is clear that even within this age range, different developmental factors could be considered. Applying integrated models of generic health literacy to MHL that acknowledge the life course and social and environmental determinants should therefore be a future priority [ 86 , 87 ].

Around a third of articles measured recognition of specific mental illnesses, with the majority using open-ended questions such as ‘ What, if anything, do you think is wrong …’, and calculating the % of correct responses. Knowledge of mental illnesses was measured more frequently than knowledge of prevention and promotion, therefore an understanding of the complete mental health state was often neglected [ 27 ]. More research is needed to develop and validate measures that assess the ability to seek out, comprehend, appraise and apply information relating to the complete mental health state as opposed to only assessing literacy of mental disorders. By using measurement tools that predominantly focus on psychiatric labels, there is evidence to suggest that stigma could be increased [ 22 , 23 ]. Given that over three quarters of intervention studies identified in this review included a measure of stigma, future research should consider the way in which mental-ill health approaches to MHL, in terms of intervention content and study measures, may influence stigma related outcomes.

It is perhaps unsurprising that the MHL field continues to be modelled on psychiatric labelling given the influence of Jorm and colleagues early work in Australia that came out of the National Health and Medical Research Council (NHMRC) Social Psychiatry Research Unit [ 12 ]. Kutcher and colleagues MHL definition also has its origins in psychiatry, but more explicitly includes understanding of mental health promotion and stigma reduction [ 15 ]. A growing body of research relating to eating disorders literacy also emphasises the need to distinguish between health promotion, prevention and early intervention initiatives in reducing the population health burden of eating-disordered behaviour and to prioritise mental health promotion programs, including those targeting stigma reduction [ 88 , 89 , 90 ]. This review identified an emerging group of articles that included understanding of how to obtain and maintain good mental health in their conceptualisation of MHL. However, this domain was rarely measured.

Just under half of the articles included items relating to general MHL. However, terminology was varied (e.g. mental illness, mental disorder, mental health problem, mental health issue). Leighton revealed that young people have a lack of conceptual clarity when it comes to these mental health related terms, unsurprising given the lack of consistent definitions in practice [ 91 ]. The range and subjectivity of mental health related terms reduces the meaningfulness of comparisons across MHL studies. Similarly, over half of the articles identified in this review assessed mental illness stigma, but the complexity of the construct caused heterogeneity in measurement. Intentions to seek help were the most commonly measured help-seeking belief; these findings support previous assessments of MHL measurement tools [ 16 ]. Measuring only intentions to seek help, without capturing knowledge of what help is available, will not provide a true picture of actual behaviour change. Findings also suggested that recognition and help-seeking related beliefs may be more directly associated with the MHL construct and, in line with previous literature [ 25 ], mental illness stigma was found to be a common outcome measure in MHL related interventions.

It is worth considering whether the MHL construct should continue to be stretched or whether we should accept that the multiple domains exist in their own right. For example, self-acquired knowledge and skills relating to positive psychology are being investigated, but are only just starting to emerge under the MHL construct [ 28 , 29 ]. Similarly, stigma and help-seeking knowledge and beliefs are assessed as part of, and independently from, the MHL framework. Adopting a multi-construct theory approach to MHL, as suggested by Spiker and Hammer [ 31 ], would see increased focus on developing and validating measures of specific MHL domains in order to better understand the way in which these domains relate to each other.

Developing better MHL theory will help provide clear logic models and theories of change for MHL interventions aiming to improve adolescent mental health, something currently lacking in the field. Although it should be acknowledged that the aims of MHL interventions will vary based on the scope, setting and cultural context, an increased number of validated measures as well as improved MHL theory could inform decisions about the most appropriate domain to measure as the outcome i.e. is the main aim of the intervention to reduce stigma or improve help-seeking. This is particularly important for school-based evaluations of MHL interventions for which respondent burden is often a concern.

We acknowledge that there were some articles in this review that adapted adult measures and tested for face and content validity with child and adolescent mental health professionals, and internal reliability and comprehension with adolescent samples. However, in general there was a lack of psychometric work to assess factor structure of scale-based measures in this age group, with large numbers of articles presenting data on an item level. More research should be conducted like that of Campos et al., working with young people to develop and psychometrically test pools of MHL items to identify latent factors [ 55 ]. This will help to inform future conceptualisation and measurement in this age group.

Even when there was evidence of a measure’s validity for use with adolescents, many articles selected only the items relevant for their study or adapted the scale to fit the cultural context. This may, in part, be an attempt to reduce the number of items and therefore the response burden. However, adaptation to measures based on the cultural discourse around mental health aligns with school-based mental health promotion approaches that account for children’s social, cultural and political contexts [ 92 ]. This raises the important question as to whether we should be trying to test and compare mental health related knowledge across cultures, particularly given the ongoing levels of disagreement amongst mental health professions between and within countries. A previous review of cross-cultural conceptualisations of positive mental health concluded that future definitions should be inclusive and culturally sensitive, and that more work was needed to empirically validate criteria for mental health [ 93 ]. Future research should consider conducting measurement invariance on existing MHL measures across different cultures. A comparison of knowledge items and their pre-defined correct answers, could help understand cultural differences in the discourse around mental health and what it means to be mental health literate across contexts.

Given the increased political interest in mental health promotion and education [ 6 , 38 ], we recommend that MHL research focuses on increasing understanding of ways to promote and maintain positive mental health, including subjective wellbeing, optimal functioning, coping and resilience [ 30 , 94 ]. Examples of knowledge items with true/false responses were identified in the current review and many aligned with a biogenetic conceptualisation of mental illness. Not only could these ‘truths’ cause more negative attitudes towards individuals experiencing mental health difficulties [ 19 ], many mapped directly onto the content of interventions and therefore do not provide any evidence of adolescents’ ability to critically appraise mental health information. To enhance individual and community level critical mental health literacy, the MHL field should apply models of public health literacy that aim to increase empowerment and control over health decisions, and acknowledge the interaction between an individual’s ability and their social and contextual demands [ 86 , 95 , 96 , 97 ]. Given that mental health is a key component of health, it is also worth questioning the usefulness of this separation moving forward; a MHL field that is playing catch up with more developed health literacy approaches could further exaggerate the existing lack of parity of esteem.

MHL research with adolescent populations is on the rise, but this review has highlighted some important areas for future consideration. Increasingly stretched definitions of MHL have led to conceptual confusion and methodological inconsistency, and there is a lack of measures developed and psychometrically tested with adolescents. Furthermore, the field is still dominated by a mental-ill health approach, with limited measures assessing the promotion of positive mental health. We suggest that the MHL field moves away from assessing ‘mental disorder literacy’ and towards critical ‘mental health literacy’. A better understanding of what MHL means for adolescents is needed in order to develop reliable, valid and feasible measures that acknowledge their developmental stage and unique social and contextual demands. In conclusion, by treating MHL as a multi-construct theory, more could be understood about the mechanisms for change in improving adolescent mental health.

Availability of data and materials

Link to PROSPERO review protocol included in the manuscript, example search strategy included as supplementary material.

Abbreviations

  • Mental health literacy

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Mansfield, R., Patalay, P. & Humphrey, N. A systematic literature review of existing conceptualisation and measurement of mental health literacy in adolescent research: current challenges and inconsistencies. BMC Public Health 20 , 607 (2020). https://doi.org/10.1186/s12889-020-08734-1

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Deep learning in mental health outcome research: a scoping review

  • Chang Su 1 ,
  • Zhenxing Xu 1 ,
  • Jyotishman Pathak 1 &
  • Fei Wang 1  

Translational Psychiatry volume  10 , Article number:  116 ( 2020 ) Cite this article

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  • Psychiatric disorders

Mental illnesses, such as depression, are highly prevalent and have been shown to impact an individual’s physical health. Recently, artificial intelligence (AI) methods have been introduced to assist mental health providers, including psychiatrists and psychologists, for decision-making based on patients’ historical data (e.g., medical records, behavioral data, social media usage, etc.). Deep learning (DL), as one of the most recent generation of AI technologies, has demonstrated superior performance in many real-world applications ranging from computer vision to healthcare. The goal of this study is to review existing research on applications of DL algorithms in mental health outcome research. Specifically, we first briefly overview the state-of-the-art DL techniques. Then we review the literature relevant to DL applications in mental health outcomes. According to the application scenarios, we categorize these relevant articles into four groups: diagnosis and prognosis based on clinical data, analysis of genetics and genomics data for understanding mental health conditions, vocal and visual expression data analysis for disease detection, and estimation of risk of mental illness using social media data. Finally, we discuss challenges in using DL algorithms to improve our understanding of mental health conditions and suggest several promising directions for their applications in improving mental health diagnosis and treatment.

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Predictive modeling of depression and anxiety using electronic health records and a novel machine learning approach with artificial intelligence

Introduction.

Mental illness is a type of health condition that changes a person’s mind, emotions, or behavior (or all three), and has been shown to impact an individual’s physical health 1 , 2 . Mental health issues including depression, schizophrenia, attention-deficit hyperactivity disorder (ADHD), and autism spectrum disorder (ASD), etc., are highly prevalent today and it is estimated that around 450 million people worldwide suffer from such problems 1 . In addition to adults, children and adolescents under the age of 18 years also face the risk of mental health disorders. Moreover, mental health illnesses have also been one of the most serious and prevalent public health problems. For example, depression is a leading cause of disability and can lead to an increased risk for suicidal ideation and suicide attempts 2 .

To better understand the mental health conditions and provide better patient care, early detection of mental health problems is an essential step. Different from the diagnosis of other chronic conditions that rely on laboratory tests and measurements, mental illnesses are typically diagnosed based on an individual’s self-report to specific questionnaires designed for the detection of specific patterns of feelings or social interactions 3 . Due to the increasing availability of data pertaining to an individual’s mental health status, artificial intelligence (AI) and machine learning (ML) technologies are being applied to improve our understanding of mental health conditions and have been engaged to assist mental health providers for improved clinical decision-making 4 , 5 , 6 . As one of the latest advances in AI and ML, deep learning (DL), which transforms the data through layers of nonlinear computational processing units, provides a new paradigm to effectively gain knowledge from complex data 7 . In recent years, DL algorithms have demonstrated superior performance in many data-rich application scenarios, including healthcare 8 , 9 , 10 .

In a previous study, Shatte et al. 11 explored the application of ML techniques in mental health. They reviewed literature by grouping them into four main application domains: diagnosis, prognosis, and treatment, public health, as well as research and clinical administration. In another study, Durstewitz et al. 9 explored the emerging area of application of DL techniques in psychiatry. They focused on DL in the studies of brain dynamics and subjects’ behaviors, and presented the insights of embedding the interpretable computational models into statistical context. In contrast, this study aims to provide a scoping review of the existing research applying DL methodologies on the analysis of different types of data related to mental health conditions. The reviewed articles are organized into four main groups according to the type of the data analyzed, including the following: (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Finally, the challenges the current studies faced with, as well as future research directions towards bridging the gap between the application of DL algorithms and patient care, are discussed.

Deep learning overview

ML aims at developing computational algorithms or statistical models that can automatically infer hidden patterns from data 12 , 13 . Recent years have witnessed an increasing number of ML models being developed to analyze healthcare data 4 . However, conventional ML approaches require a significant amount of feature engineering for optimal performance—a step that is necessary for most application scenarios to obtain good performance, which is usually resource- and time-consuming.

As the newest wave of ML and AI technologies, DL approaches aim at the development of an end-to-end mechanism that maps the input raw features directly into the outputs through a multi-layer network structure that is able to capture the hidden patterns within the data. In this section, we will review several popular DL model architectures, including deep feedforward neural network (DFNN), recurrent neural network (RNN) 14 , convolutional neural network (CNN) 15 , and autoencoder 16 . Figure 1 provides an overview of these architectures.

figure 1

a Deep feedforward neural network (DFNN). It is the basic design of DL models. Commonly, a DFNN contains multiple hidden layers. b A recurrent neural network (RNN) is presented to process sequence data. To encode history information, each recurrent neuron receives the input element and the state vector of the predecessor neuron, and yields a hidden state fed to the successor neuron. For example, not only the individual information but also the dependence of the elements of the sequence x 1  → x 2  → x 3  → x 4  → x 5 is encoded by the RNN architecture. c Convolutional neural network (CNN). Between input layer (e.g., input neuroimage) and output layer, a CNN commonly contains three types of layers: the convolutional layer that is to generate feature maps by sliding convolutional kernels in the previous layer; the pooling layer is used to reduce dimensionality of previous convolutional layer; and the fully connected layer is to make prediction. For the illustrative purpose, this example only has one layer of each type; yet, a real-world CNN would have multiple convolutional and pooling layers (usually in an interpolated manner) and one fully connected layer. d Autoencoder consists of two components: the encoder, which learns to compress the input data into a latent representation layer by layer, whereas the decoder, inverse to the encoder, learns to reconstruct the data at the output layer. The learned compressed representations can be fed to the downstream predictive model.

Deep feedforward neural network

Artificial neural network (ANN) is proposed with the intention of mimicking how human brain works, where the basic element is an artificial neuron depicted in Fig. 2a . Mathematically, an artificial neuron is a nonlinear transformation unit, which takes the weighted summation of all inputs and feeds the result to an activation function, such as sigmoid, rectifier (i.e., rectified linear unit [ReLU]), or hyperbolic tangent (Fig. 2b ). An ANN is composed of multiple artificial neurons with different connection architectures. The simplest ANN architecture is the feedforward neural network (FNN), which stacks the neurons layer by layer in a feedforward manner (Fig. 1a ), where the neurons across adjacent layers are fully connected to each other. The first layer of the FNN is the input layer that each unit receives one dimension of the data vector. The last layer is the output layer that outputs the probabilities that a subject belonging to different classes (in classification). The layers between the input and output layers are the hidden layers. A DFNN usually contains multiple hidden layers. As shown in Fig. 2a , there is a weight parameter associated with each edge in the DFNN, which needs to be optimized by minimizing some training loss measured on a specific training dataset (usually through backpropagation 17 ). After the optimal set of parameters are learned, the DFNN can be used to predict the target value (e.g., class) of any testing data vectors. Therefore, a DFNN can be viewed as an end-to-end process that transforms a specific raw data vector to its target layer by layer. Compared with the traditional ML models, DFNN has shown superior performance in many data mining tasks and have been introduced to the analysis of clinical data and genetic data to predict mental health conditions. We will discuss the applications of these methods further in the Results section.

figure 2

a An illustration of basic unit of neural networks, i.e., artificial neuron. Each input x i is associated with a weight w i . The weighted sum of all inputs Σ w i x i is fed to a nonlinear activation function f to generate the output y j of the j -th neuron, i.e., y j  =  f (Σ w i x i ). b Illustrations of the widely used nonlinear activation function.

Recurrent neural network

RNNs were designed to analyze sequential data such as natural language, speech, and video. Given an input sequence, the RNN processes one element of the sequence at a time by feeding to a recurrent neuron. To encode the historical information along the sequence, each recurrent neuron receives the input element at the corresponding time point and the output of the neuron at previous time stamp, and the output will also be provided to the neuron at next time stamp (this is also where the term “recurrent” comes from). An example RNN architecture is shown in Fig. 1b where the input is a sequence of words (a sentence). The recurrence link (i.e., the edge linking different neurons) enables RNN to capture the latent semantic dependencies among words and the syntax of the sentence. In recent years, different variants of RNN, such as long short-term memory (LSTM) 18 and gated recurrent unit 19 have been proposed, and the main difference among these models is how the input is mapped to the output for the recurrent neuron. RNN models have demonstrated state-of-the-art performance in various applications, especially natural language processing (NLP; e.g., machine translation and text-based classification); hence, they hold great premise in processing clinical notes and social media posts to detect mental health conditions as discussed below.

Convolutional neural network

CNN is a specific type of deep neural network originally designed for image analysis 15 , where each pixel corresponds to a specific input dimension describing the image. Similar to a DFNN, CNN also maps these input image pixels to the corresponding target (e.g., image class) through layers of nonlinear transformations. Different from DFNN, where only fully connected layers are considered, there are typically three types of layers in a CNN: a convolution–activation layer, a pooling layer, and a fully connected layer (Fig. 1c ). The convolution–activation layer first convolves the entire feature map obtained from previous layer with small two-dimensional convolution filters. The results from each convolution filter are activated through a nonlinear activation function in the same way as a DFNN. A pooling layer reduces the size of the feature map through sub-sampling. The fully connected layer is analogous to the hidden layer in a DFNN, where each neuron is connected to all neurons of the previous layer. The convolution–activation layer extracts locally invariant patterns from the feature maps. The pooling layer effectively reduces the feature dimensionality to avoid model overfitting. The fully connected layer explores the global feature interactions as in DFNNs. Different combinations of these three types of layers constitute different CNN architectures. Because of the various characteristics of images such as local self-similarity, compositionality, and translational and deformation invariance, CNN has demonstrated state-of-the-art performance in many computer vision tasks 7 . Hence, the CNN models are promising in processing clinical images and expression data (e.g., facial expression images) to detect mental health conditions. We will discuss the application of these methods in the Results section.

Autoencoder

Autoencoder is a special variant of the DFNN aimed at learning new (usually more compact) data representations that can optimally reconstruct the original data vectors 16 , 20 . An autoencoder typically consists of two components (Fig. 1d ) as follows: (1) the encoder, which learns new representations (usually with reduced dimensionality) from the input data through a multi-layer FNN; and (2) the decoder, which is exactly the reverse of the encoder, reconstructs the data in their original space from the representations derived from the encoder. The parameters in the autoencoder are learned through minimizing the reconstruction loss. Autoencoder has demonstrated the capacity of extracting meaningful features from raw data without any supervision information. In the studies of mental health outcomes, the use of autoencoder has resulted in desirable improvement in analyzing clinical and expression image data, which will be detailed in the Results section.

The processing and reporting of the results of this review were guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines 21 . To thoroughly review the literature, a two-step method was used to retrieve all the studies on relevant topics. First, we conducted a search of the computerized bibliographic databases including PubMed and Web of Science. The search strategy is detailed in Supplementary Appendix 1 . The literature search comprised articles published until April 2019. Next, a snowball technique was applied to identify additional studies. Furthermore, we manually searched other resources, including Google Scholar, and Institute of Electrical and Electronics Engineers (IEEE Xplore), to find additional relevant articles.

Figure 3 presents the study selection process. All articles were evaluated carefully and studies were excluded if: (1) the main outcome is not a mental health condition; (2) the model involved is not a DL algorithm; (3) full-text of the article is not accessible; and (4) the article is written not in English.

figure 3

In total, 57 studies, in terms of clinical data analysis, genetic data analysis, vocal and visual expression data analysis, and social media data analysis, which met our eligibility criteria, were included in this review.

A total of 57 articles met our eligibility criteria. Most of the reviewed articles were published between 2014 and 2019. To clearly summarize these articles, we grouped them into four categories according to the types of data analyzed, including (1) clinical data, (2) genetic and genomics data, (3) vocal and visual expression data, and (4) social media data. Table 1 summarizes the characteristics of these selected studies.

Clinical data

Neuroimages.

Previous studies have shown that neuroimages can record evidence of neuropsychiatric disorders 22 , 23 . Two common types of neuroimage data analyzed in mental health studies are functional magnetic resonance imaging (fMRI) and structural MRI (sMRI) data. In fMRI data, the brain activity is measured by identification of the changes associated with blood flow, based on the fact that cerebral blood flow and neuronal activation are coupled 24 . In sMRI data, the neurological aspect of a brain is described based on the structural textures, which show some information in terms of the spatial arrangements of voxel intensities in 3D. Recently, DL technologies have been demonstrated in analyzing both fMRI and sMRI data.

One application of DL in fMRI and sMRI data is the identification of ADHD 25 , 26 , 27 , 28 , 29 , 30 , 31 . To learn meaningful information from the neuroimages, CNN and deep belief network (DBN) models were used. In particular, the CNN models were mainly used to identify local spatial patterns and DBN models were to obtain a deep hierarchical representation of the neuroimages. Different patterns were discovered between ADHDs and controls in the prefrontal cortex and cingulated cortex. Also, several studies analyzed sMRIs to investigate schizophrenia 32 , 33 , 34 , 35 , 36 , where DFNN, DBN, and autoencoder were utilized. These studies reported abnormal patterns of cortical regions and cortical–striatal–cerebellar circuit in the brain of schizophrenia patients, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. Moreover, the use of DL in neuroimages also targeted at addressing other mental health disorders. Geng et al. 37 proposed to use CNN and autoencoder to acquire meaningful features from the original time series of fMRI data for predicting depression. Two studies 31 , 38 integrated the fMRI and sMRI data modalities to develop predictive models for ASDs. Significant relationships between fMRI and sMRI data were observed with regard to ASD prediction.

Challenges and opportunities

The aforementioned studies have demonstrated that the use of DL techniques in analyzing neuroimages can provide evidence in terms of mental health problems, which can be translated into clinical practice and facilitate the diagnosis of mental health illness. However, multiple challenges need to be addressed to achieve this objective. First, DL architectures generally require large data samples to train the models, which may pose a difficulty in neuroimaging analysis because of the lack of such data 39 . Second, typically the imaging data lie in a high-dimensional space, e.g., even a 64 × 64 2D neuroimage can result in 4096 features. This leads to the risk of overfitting by the DL models. To address this, most existing studies reported to utilize MRI data preprocessing tools such as Statistical Parametric Mapping ( https://www.fil.ion.ucl.ac.uk/spm/ ), Data Processing Assistant for Resting-State fMRI 40 , and fMRI Preprocessing Pipeline 41 to extract useful features before feeding to the DL models. Even though an intuitive attribute of DL is the capacity to learn meaningful features from raw data, feature engineering tools are needed especially in the case of small sample size and high-dimensionality, e.g., the neuroimage analysis. The use of such tools mitigates the overfitting risk of DL models. As reported in some selected studies 28 , 31 , 35 , 37 , the DL models can benefit from feature engineering techniques and have been shown to outperform the traditional ML models in the prediction of multiple conditions such as depression, schizophrenia, and ADHD. However, such tools extract features relying on prior knowledge; hence may omit some information that is meaningful for mental outcome research but unknown yet. An alternative way is to use CNN to automatically extract information from the raw data. As reported in the previous study 10 , CNNs perform well in processing raw neuroimage data. Among the studies reviewed in this study, three 29 , 30 , 37 reported to involve CNN layers and achieved desirable performances.

Electroencephalogram data

As a low-cost, small-size, and high temporal resolution signal containing up to several hundred channels, analysis of electroencephalogram (EEG) data has gained significant attention to study brain disorders 42 . As the EEG signal is one kind of streaming data that presents a high density and continuous characteristics, it challenges traditional feature engineering-based methods to obtain sufficient information from the raw EEG data to make accurate predictions. To address this, recently the DL models have been employed to analyze raw EEG signal data.

Four articles reviewed proposed to use DL in understanding mental health conditions based on the analysis of EEG signals. Acharya et al. 43 used CNN to extract features from the input EEG signals. They found that the EEG signals from the right hemisphere of the human brain are more distinctive in terms of the detection of depression than those from the left hemisphere. The findings provided shreds of evidence that depression is associated with a hyperactive right hemisphere. Mohan et al. 44 modeled the raw EEG signals by DFNN to obtain information about the human brain waves. They found that the signals collected from the central (C3 and C4) regions are marginally higher compared with other brain regions, which can be used to distinguish the depressed and normal subjects from the brain wave signals. Zhang et al. 45 proposed a concatenated structure of deep recurrent and 3D CNN to obtain EEG features across different tasks. They reported that the DL model can capture the spectral changes of EEG hemispheric asymmetry to distinguish different mental workload effectively. Li et al. 46 presented a computer-aided detection system by extracting multiple types of information (e.g., spectral, spatial, and temporal information) to recognize mild depression based on CNN architecture. The authors found that both spectral and temporal information of EEG are crucial for prediction of depression.

EEG data are usually classified as streaming data that are continuous and are of high density. Despite the initial success in applying DL algorithms to analyze EEG data for studying multiple mental health conditions, there exist several challenges. One major challenge is that raw EEG data gathered from sensors have a certain degree of erroneous, noisy, and redundant information caused by discharged batteries, failures in sensor readings, and intermittent communication loss in wireless sensor networks 47 . This may challenge the model in extracting meaningful information from noise. Multiple preprocessing steps (e.g., data denoising, data interpolation, data transformation, and data segmentation) are necessary for dealing with the raw EEG signal before feeding to the DL models. Besides, due to the dense characteristics in the raw EEG data, analysis of the streaming data is computationally more expensive, which poses a challenge for the model architecture selection. A proper model should be designed relatively with less training parameters. This is one reason why the reviewed studies are mainly based on the CNN architecture.

Electronic health records

Electronic health records (EHRs) are systematic collections of longitudinal, patient-centered records. Patients’ EHRs consist of both structured and unstructured data: the structured data include information about a patient’s diagnosis, medications, and laboratory test results, and the unstructured data include information in clinical notes. Recently, DL models have been applied to analyze EHR data to study mental health disorders 48 .

The first and foremost issue for analyzing the structured EHR data is how to appropriately handle the longitudinal records. Traditional ML models address this by collapsing patients’ records within a certain time window into vectors, which comprised the summary of statistics of the features in different dimensions 49 . For instance, to estimate the probability of suicide deaths, Choi et al. 50 leveraged a DFNN to model the baseline characteristics. One major limitation of these studies is the omittance of temporality among the clinical events within EHRs. To overcome this issue, RNNs are more commonly used for EHR data analysis as an RNN intuitively handles time-series data. DeepCare 51 , a long short-term memory network (LSTM)-based DL model, encodes patient’s long-term health state trajectories to predict the future outcomes of depressive episodes. As the LSTM architecture appropriately captures disease progression by modeling the illness history and the medical interventions, DeepCare achieved over 15% improvement in prediction, compared with the conventional ML methods. In addition, Lin et al. 52 designed two DFNN models for the prediction of antidepressant treatment response and remission. The authors reported that the proposed DFNN can achieve an area under the receiver operating characteristic curve (AUC) of 0.823 in predicting antidepressant response.

Analyzing the unstructured clinical notes in EHRs refers to the long-standing topic of NLP. To extract meaningful knowledge from the text, conventional NLP approaches mostly define rules or regular expressions before the analysis. However, it is challenging to enumerate all possible rules or regular expressions. Due to the recent advance of DL in NLP tasks, DL models have been developed to mine clinical text data from EHRs to study mental health conditions. Geraci et al. 53 utilized term frequency-inverse document frequency to represent the clinical documents by words and developed a DFNN model to identify individuals with depression. One major limitation of such an approach is that the semantics and syntax of sentences are lost. In this context, CNN 54 and RNN 55 have shown superiority in modeling syntax for text-based prediction. In particular, CNN has been used to mine the neuropsychiatric notes for predicting psychiatric symptom severity 56 , 57 . Tran and Kavuluru 58 used an RNN to analyze the history of present illness in neuropsychiatric notes for predicting mental health conditions. The model engaged an attention mechanism 55 , which can specify the importance of the words in prediction, making the model more interpretable than their previous CNN model 56 .

Although DL has achieved promising results in EHR analysis, several challenges remain unsolved. On one hand, different from diagnosing physical health condition such as diabetes, the diagnosis of mental health conditions lacks direct quantitative tests, such as a blood chemistry test, a buccal swab, or urinalysis. Instead, the clinicians evaluate signs and symptoms through patient interviews and questionnaires during which they gather information based on patient’s self-report. Collection and deriving inferences from such data deeply relies on the experience and subjectivity of the clinician. This may account for signals buried in noise and affect the robustness of the DL model. To address this challenge, a potential way is to comprehensively integrate multimodal clinical information, including structured and unstructured EHR information, as well as neuroimaging and EEG data. Another way is to incorporate existing medical knowledge, which can guide model being trained in the right direction. For instance, the biomedical knowledge bases contain massive verified interactions between biomedical entities, e.g., diseases, genes, and drugs 59 . Incorporating such information brings in meaningful medical constraints and may help to reduce the effects of noise on model training process. On the other hand, implementing a DL model trained from one EHR system into another system is challenging, because EHR data collection and representation is rarely standardized across hospitals and clinics. To address this issue, national/international collaborative efforts such as Observational Health Data Sciences and Informatics ( https://ohdsi.org ) have developed common data models, such as OMOP, to standardize EHR data representation for conducting observational data analysis 60 .

Genetic data

Multiple studies have found that mental disorders, e.g., depression, can be associated with genetic factors 61 , 62 . Conventional statistical studies in genetics and genomics, such as genome-wide association studies, have identified many common and rare genetic variants, such as single-nucleotide polymorphisms (SNPs), associated with mental health disorders 63 , 64 . Yet, the effect of the genetic factors is small and many more have not been discovered. With the recent developments in next-generation sequencing techniques, a massive volume of high-throughput genome or exome sequencing data are being generated, enabling researchers to study patients with mental health disorders by examining all types of genetic variations across an individual’s genome. In recent years, DL 65 , 66 has been applied to identify genetic risk factors associated with mental illness, by borrowing the capacity of DL in identifying highly complex patterns in large datasets. Khan and Wang 67 integrated genetic annotations, known brain expression quantitative trait locus, and enhancer/promoter peaks to generate feature vectors of variants, and developed a DFNN, named ncDeepBrain, to prioritized non-coding variants associated with mental disorders. To further prioritize susceptibility genes, they designed another deep model, iMEGES 68 , which integrates the ncDeepBrain score, general gene scores, and disease-specific scores for estimating gene risk. Wang et al. 69 developed a novel deep architecture that combines deep Boltzmann machine architecture 70 with conditional and lateral connections derived from the gene regulatory network. The model provided insights about intermediate phenotypes and their connections to high-level phenotypes (disease traits). Laksshman et al. 71 used exome sequencing data to predict bipolar disorder outcomes of patients. They developed a CNN and used the convolution mechanism to capture correlations of the neighboring loci within the chromosome.

Although the use of genetic data in DL in studying mental health conditions shows promise, multiple challenges need to be addressed. For DL-based risk c/gene prioritization efforts, one major challenge is the limitation of labeled data. On one hand, the positive samples are limited, as known risk SNPs or genes associated with mental health conditions are limited. For example, there are about 108 risk loci that were genome-wide significant in ASD. On the other hand, the negative samples (i.e., SNPs, variants, or genes) may not be the “true” negative, as it is unclear whether they are associated with the mental illness yet. Moreover, it is also challenging to develop DL models for analyzing patient’s sequencing data for mental illness prediction, as the sequencing data are extremely high-dimensional (over five million SNPs in the human genome). More prior domain knowledge is needed to guide the DL model extracting patterns from the high-dimensional genomic space.

Vocal and visual expression data

The use of vocal (voice or speech) and visual (video or image of facial or body behaviors) expression data has gained the attention of many studies in mental health disorders. Modeling the evolution of people’s emotional states from these modalities has been used to identify mental health status. In essence, the voice data are continuous and dense signals, whereas the video data are sequences of frames, i.e., images. Conventional ML models for analyzing such types of data suffer from the sophisticated feature extraction process. Due to the recent success of applying DL in computer vision and sequence data modeling, such models have been introduced to analyze the vocal and/or visual expression data. In this work, most articles reviewed are to predict mental health disorders based on two public datasets: (i) the Chi-Mei corpus, collected by using six emotional videos to elicit facial expressions and speech responses of the subjects of bipolar disorder, unipolar depression, and healthy controls; 72 and (ii) the International Audio/Visual Emotion Recognition Challenges (AVEC) depression dataset 73 , 74 , 75 , collected within human–computer interaction scenario. The proposed models include CNNs, RNNs, autoencoders, as well as hybrid models based on the above ones. In particular, CNNs were leveraged to encode the temporal and spectral features from the voice signals 76 , 77 , 78 , 79 , 80 and static facial or physical expression features from the video frames 79 , 81 , 82 , 83 , 84 . Autoencoders were used to learn low-dimensional representations for people’s vocal 85 , 86 and visual expression 87 , 88 , and RNNs were engaged to characterize the temporal evolution of emotion based on the CNN-learned features and/or other handcraft features 76 , 81 , 84 , 85 , 86 , 87 , 88 , 89 , 90 . Few studies focused on analyzing static images using a CNN architecture to predict mental health status. Prasetio et al. 91 identified the stress types (e.g., neutral, low stress, and high stress) from facial frontal images. Their proposed CNN model outperforms the conventional ML models by 7% in terms of prediction accuracy. Jaiswal et al. 92 investigated the relationship between facial expression/gestures and neurodevelopmental conditions. They reported accuracy over 0.93 in the diagnostic prediction of ADHD and ASD by using the CNN architecture. In addition, thermal images that track persons’ breathing patterns were also fed to a deep model to estimate psychological stress level (mental overload) 93 .

From the above summary, we can observe that analyzing vocal and visual expression data can capture the pattern of subjects’ emotion evolution to predict mental health conditions. Despite the promising initial results, there remain challenges for developing DL models in this field. One major challenge is to link vocal and visual expression data with the clinical data of patients, given the difficulties involved in collecting such expression data during clinical practice. Current studies analyzed vocal and visual expression over individual datasets. Without clinical guidance, the developed prediction models have limited clinical meanings. Linking patients’ expression information with clinical variables may help to improve both the interpretability and robustness of the model. For example, Gupta et al. 94 designed a DFNN for affective prediction from audio and video modalities. The model incorporated depression severity as the parameter, linking the effects of depression on subjects’ affective expressions. Another challenge is the limitation of the samples. For example, the Chi-Mei dataset contains vocal–visual data from only 45 individuals (15 with bipolar disorder, 15 with unipolar disorder, and 15 healthy controls). Also, there is a lack of “emotion labels” for people’s vocal and visual expression. Apart from improving the datasets, an alternative way to solve this challenge is to use transfer learning, which transfers knowledge gained with one dataset (usually more general) to the target dataset. For example, some studies trained autoencoder in public emotion database such as eNTERFACE 95 to generate emotion profiles (EPs). Other studies 83 , 84 pre-trained CNN over general facial expression datasets 96 , 97 for extracting face appearance features.

Social media data

With the widespread proliferation of social media platforms, such as Twitter and Reddit, individuals are increasingly and publicly sharing information about their mood, behavior, and any ailments one might be suffering. Such social media data have been used to identify users’ mental health state (e.g., psychological stress and suicidal ideation) 6 .

In this study, the articles that used DL to analyze social media data mainly focused on stress detection 98 , 99 , 100 , 101 , depression identification 102 , 103 , 104 , 105 , 106 , and estimation of suicide risk 103 , 105 , 107 , 108 , 109 . In general, the core concept across these work is to mine the textual, and where applicable graphical, content of users’ social media posts to discover cues for mental health disorders. In this context, the RNN and CNN were largely used by the researchers. Especially, RNN usually introduces an attention mechanism to specify the importance of the input elements in the classification process 55 . This provides some interpretability for the predictive results. For example, Ive et al. 103 proposed a hierarchical RNN architecture with an attention mechanism to predict the classes of the posts (including depression, autism, suicidewatch, anxiety, etc.). The authors observed that, benefitting from the attention mechanism, the model can predict risk text efficiently and extract text elements crucial for making decisions. Coppersmith et al. 107 used LSTM to discover quantifiable signals about suicide attempts based on social media posts. The proposed model can capture contextual information between words and obtain nuances of language related to suicide.

Apart from text, users also post images on social media. The properties of the images (e.g., color theme, saturation, and brightness) provide some cues reflecting users’ mental health status. In addition, millions of interactions and relationships among users can reflect the social environment of individuals that is also a kind of risk factors for mental illness. An increasing number of studies attempted to combine these two types of information with text content for predictive modeling. For example, Lin et al. 99 leveraged the autoencoder to extract low-level and middle-level representations from texts, images, and comments based on psychological and art theories. They further extended their work with a hybrid model based on CNN by integrating post content and social interactions 101 . The results provided an implication that the social structure of the stressed users’ friends tended to be less connected than that of the users without stress.

The aforementioned studies have demonstrated that using social media data has the potential to detect users with mental health problems. However, there are multiple challenges towards the analysis of social media data. First, given that social media data are typically de-identified, there is no straightforward way to confirm the “true positives” and “true negatives” for a given mental health condition. Enabling the linkage of user’s social media data with their EHR data—with appropriate consent and privacy protection—is challenging to scale, but has been done in a few settings 110 . In addition, most of the previous studies mainly analyzed textual and image data from social media platforms, and did not consider analyzing the social network of users. In one study, Rosenquist et al. 111 reported that the symptoms of depression are highly correlated inside the circle of friends, indicating that social network analysis is likely to be a potential way to study the prevalence of mental health problems. However, comprehensively modeling text information and network structure remains challenging. In this context, graph convolutional networks 112 have been developed to address networked data mining. Moreover, although it is possible to discover online users with mental illness by social media analysis, translation of this innovation into practical applications and offer aid to users, such as providing real-time interventions, are largely needed 113 .

Discussion: findings, open issues, and future directions

Principle findings.

The purpose of this study is to investigate the current state of applications of DL techniques in studying mental health outcomes. Out of 2261 articles identified based on our search terms, 57 studies met our inclusion criteria and were reviewed. Some studies that involved DL models but did not highlight the DL algorithms’ features on analysis were excluded. From the above results, we observed that there are a growing number of studies using DL models for studying mental health outcomes. Particularly, multiple studies have developed disease risk prediction models using both clinical and non-clinical data, and have achieved promising initial results.

DL models “think to learn” like a human brain relying on their multiple layers of interconnected computing neurons. Therefore, to train a deep neural network, there are multiple parameters (i.e., weights associated links between neurons within the network) being required to learn. This is one reason why DL has achieved great success in the fields where a massive volume of data can be easily collected, such as computer vision and text mining. Yet, in the health domain, the availability of large-scale data is very limited. For most selected studies in this review, the sample sizes are under a scale of 10 4 . Data availability is even more scarce in the fields of neuroimaging, EEG, and gene expression data, as such data reside in a very high-dimensional space. This then leads to the problem of “curse of dimensionality” 114 , which challenges the optimization of the model parameters.

One potential way to address this challenge is to reduce the dimensionality of the data by feature engineering before feeding information to the DL models. On one hand, feature extraction approaches can be used to obtain different types of features from the raw data. For example, several studies reported in this review have attempted to use preprocessing tools to extract features from neuroimaging data. On the other hand, feature selection that is commonly used in conventional ML models is also an option to reduce data dimensionality. However, the feature selection approaches are not often used in the DL application scenario, as one of the intuitive attributes of DL is the capacity to learn meaningful features from “all” available data. The alternative way to address the issue of data bias is to use transfer learning where the objective is to improve learning a new task through the transfer of knowledge from a related task that has already been learned 115 . The basic idea is that data representations learned in the earlier layers are more general, whereas those learned in the latter layers are more specific to the prediction task 116 . In particular, one can first pre-train a deep neural network in a large-scale “source” dataset, then stack fully connected layers on the top of the network and fine-tune it in the small “target” dataset in a standard backpropagation manner. Usually, samples in the “source” dataset are more general (e.g., general image data), whereas those in the “target” dataset are specific to the task (e.g., medical image data). A popular example of the success of transfer learning in the health domain is the dermatologist-level classification of skin cancer 117 . The authors introduced Google’s Inception v3 CNN architecture pre-trained over 1.28 million general images and fine-tuned in the clinical image dataset. The model achieved very high-performance results of skin cancer classification in epidermal (AUC = 0.96), melanocytic (AUC = 0.96), and melanocytic–dermoscopic images (AUC = 0.94). In facial expression-based depression prediction, Zhu et al. 83 pre-trained CNN on the public face recognition dataset to model the static facial appearance, which overcomes the issue that there is no facial expression label information. Chao et al. 84 also pre-trained CNN to encode facial expression information. The transfer scheme of both of the two studies has been demonstrated to be able to improve the prediction performance.

Diagnosis and prediction issues

Unlike the diagnosis of physical conditions that can be based on lab tests, diagnoses of the mental illness typically rely on mental health professionals’ judgment and patient self-report data. As a result, such a diagnostic system may not accurately capture the psychological deficits and symptom progression to provide appropriate therapeutic interventions 118 , 119 . This issue accordingly accounts for the limitation of the prediction models to assist clinicians to make decisions. Except for several studies using the unsupervised autoencoder for learning low-dimensional representations, most studies reviewed in this study reported using supervised DL models, which need the training set containing “true” (i.e., expert provided) labels to optimize the model parameters before the model being used to predict labels of new subjects. Inevitably, the quality of the expert-provided diagnostic labels used for training sets the upper-bound for the prediction performance of the model.

One intuitive route to address this issue is to use an unsupervised learning scheme that, instead of learning to predict clinical outcomes, aims at learning compacted yet informative representations of the raw data. A typical example is the autoencoder (as shown in Fig. 1d ), which encodes the raw data into a low-dimensional space, from which the raw data can be reconstructed. Some studies reviewed have proposed to leverage autoencoder to improve our understanding of mental health outcomes. A constraint of the autoencoder is that the input data should be preprocessed to vectors, which may lead to information loss for image and sequence data. To address this, recently convolutional-autoencoder 120 and LSTM-autoencoder 121 have been developed, which integrate the convolution layers and recurrent layers with the autoencoder architecture and enable us to learn informative low-dimensional representations from the raw image data and sequence data, respectively. For instance, Baytas et al. 122 developed a variation of LSTM-autoencoder on patient EHRs and grouped Parkinson’s disease patients into meaningful subtypes. Another potential way is to predict other clinical outcomes instead of the diagnostic labels. For example, several selected studies proposed to predict symptom severity scores 56 , 57 , 77 , 82 , 84 , 87 , 89 . In addition, Du et al. 108 attempted to identify suicide-related psychiatric stressors from users’ posts on Twitter, which plays an important role in the early prevention of suicidal behaviors. Furthermore, training model to predict future outcomes such as treatment response, emotion assessments, and relapse time is also a promising future direction.

Multimodal modeling

The field of mental health is heterogeneous. On one hand, mental illness refers to a variety of disorders that affect people’s emotions and behaviors. On the other hand, though the exact causes of most mental illnesses are unknown to date, it is becoming increasingly clear that the risk factors for these diseases are multifactorial as multiple genetic, environmental, and social factors interact to influence an individual’s mental health 123 , 124 . As a result of domain heterogeneity, researchers have the chance to study the mental health problems from different perspectives, from molecular, genomic, clinical, medical imaging, physiological signal to facial, and body expressive and online behavioral. Integrative modeling of such multimodal data means comprehensively considering different aspects of the disease, thus likely obtaining deep insight into mental health. In this context, DL models have been developed for multimodal modeling. As shown in Fig. 4 , the hierarchical structure of DL makes it easily compatible with multimodal integration. In particular, one can model each modality with a specific network and combine them by the final fully connected layers, such that parameters can be jointly learned by a typical backpropagation manner. In this review, we found an increasing number of studies have attempted to use multimodal modeling. For example, Zou et al. 28 developed a multimodal model composed of two CNNs for modeling fMRI and sMRI modalities, respectively. The model achieved 69.15% accuracy in predicting ADHD, which outperformed the unimodal models (66.04% for fMRI modal-based and 65.86% for sMRI modal-based). Yang et al. 79 proposed a multimodal model to combine vocal and visual expression for depression cognition. The model results in 39% lower prediction error than the unimodal models.

figure 4

One can model each modality with a specific network and combine them using the final fully-connected layers. In this way, parameters of the entire neural network can be jointly learned in a typical backpropagation manner.

Model interpretability

Due to the end-to-end design, the DL models usually appear to be “black boxes”: they take raw data (e.g., MRI images, free-text of clinical notes, and EEG signals) as input, and yield output to reach a conclusion (e.g., the risk of a mental health disorder) without clear explanations of their inner working. Although this might not be an issue in other application domains such as identifying animals from images, in health not only the model’s prediction performance but also the clues for making the decision are important. For example in the neuroimage-based depression identification, despite estimation of the probability that a patient suffers from mental health deficits, the clinicians would focus more on recognizing abnormal regions or patterns of the brain associated with the disease. This is really important for convincing the clinical experts about the actions recommended from the predictive model, as well as for guiding appropriate interventions. In addition, as discussed above, the introduction of multimodal modeling leads to an increased challenge in making the models more interpretable. Attempts have been made to open the “black box” of DL 59 , 125 , 126 , 127 . Currently, there are two general directions for interpretable modeling: one is to involve the systematic modification of the input and the measure of any resulting changes in the output, as well as in the activation of the artificial neurons in the hidden layers. Such a strategy is usually used in CNN in identifying specific regions of an image being captured by a convolutional layer 128 . Another way is to derive tools to determine the contribution of one or more features of the input data to the output. In this case, the widely used tools include Shapley Additive Explanation 129 , LIME 127 , DeepLIFT 130 , etc., which are able to assign each feature an importance score for the specific prediction task.

Connection to therapeutic interventions

According to the studies reviewed, it is now possible to detect patients with mental illness based on different types of data. Compared with the traditional ML techniques, most of the reviewed DL models reported higher prediction accuracy. The findings suggested that the DL models are likely to assist clinicians in improved diagnosis of mental health conditions. However, to associate diagnosis of a condition with evidence-based interventions and treatment, including identification of appropriate medication 131 , prediction of treatment response 52 , and estimation of relapse risk 132 still remains a challenge. Among the reviewed studies, only one 52 proposed to target at addressing these issues. Thus, further efforts are needed to link the DL techniques with the therapeutic intervention of mental illness.

Domain knowledge

Another important direction is to incorporate domain knowledge. The existing biomedical knowledge bases are invaluable sources for solving healthcare problems 133 , 134 . Incorporating domain knowledge could address the limitation of data volume, problems of data quality, as well as model generalizability. For example, the unified medical language system 135 can help to identify medical entities from the text and gene–gene interaction databases 136 could help to identify meaningful patterns from genomic profiles.

Recent years have witnessed the increasing use of DL algorithms in healthcare and medicine. In this study, we reviewed existing studies on DL applications to study mental health outcomes. All the results available in the literature reviewed in this work illustrate the applicability and promise of DL in improving the diagnosis and treatment of patients with mental health conditions. Also, this review highlights multiple existing challenges in making DL algorithms clinically actionable for routine care, as well as promising future directions in this field.

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Published on 9.6.2020 in Vol 7 , No 6 (2020) : June

Peer Support in Mental Health: Literature Review

Authors of this article:

Author Orcid Image

  • Reham A Hameed Shalaby * , MD   ; 
  • Vincent I O Agyapong * , MD, PhD  

Department of Psychiatry, University of Alberta, Edmonton, AB, Canada

*all authors contributed equally

Corresponding Author:

Reham A Hameed Shalaby, MD

Department of Psychiatry

University of Alberta

1E1 Walter Mackenzie Health Sciences Centre

8440 112 St NW

Edmonton, AB, T6G 2B7

Phone: 1 4034702050

Email: [email protected]

Background: A growing gap has emerged between people with mental illness and health care professionals, which in recent years has been successfully closed through the adoption of peer support services (PSSs). Peer support in mental health has been variously defined in the literature and is simply known as the help and support that people with lived experience of mental illness or a learning disability can give to one another. Although PSSs date back to several centuries, it is only in the last few decades that these services have formally evolved, grown, and become an integral part of the health care system. Debates around peer support in mental health have been raised frequently in the literature. Although many authors have emphasized the utmost importance of incorporating peer support into the health care system to instill hope; to improve engagement, quality of life, self-confidence, and integrity; and to reduce the burden on the health care system, other studies suggest that there are neutral effects from integrating PSSs into health care systems, with a probable waste of resources.

Objective: In this general review, we aimed to examine the literature, exploring the evolution, growth, types, function, generating tools, evaluation, challenges, and the effect of PSSs in the field of mental health and addiction. In addition, we aimed to describe PSSs in different, nonexhaustive contexts, as shown in the literature, that aims to draw attention to the proposed values of PSSs in such fields.

Methods: The review was conducted through a general search of the literature on MEDLINE, Google Scholar, EMBASE, Scopus, Chemical Abstracts, and PsycINFO. Search terms included peer support, peer support in mental health, social support, peer, family support, and integrated care.

Results: There is abundant literature defining and describing PSSs in different contexts as well as tracking their origins. Two main transformational concepts have been described, namely, intentional peer support and transformation from patients to peer support providers. The effects of PSSs are extensive and integrated into different fields, such as forensic PSSs, addiction, and mental health, and in different age groups and mental health condition severity. Satisfaction of and challenges to PSS integration have been clearly dependent on a number of factors and consequently impact the future prospect of this workforce.

Conclusions: There is an internationally growing trend to adopt PSSs within addiction and mental health services, and despite the ongoing challenges, large sections of the current literature support the inclusion of peer support workers in the mental health care workforce. The feasibility and maintenance of a robust PSS in health care would only be possible through collaborative efforts and ongoing support and engagement from all health care practitioners, managers, and other stakeholders.

Introduction

Peer support services (PSSs) are novel interventions recently adopted in mental health systems worldwide. It is believed, however, that PSSs date back to more than three centuries to the moral treatment era [ 1 ], albeit on an informal basis. Diverse definitions and classifications for PSSs have been provided in the literature [ 2 - 4 ], and numerous reports have praised and supported the service provided by peer support workers (PSWs) [ 5 - 8 ]. However, other literature suggests the neutral effects of PSSs, with weak associated evidence to support such services [ 9 , 10 ]. The potential impact of PSWs on their peers [ 11 - 14 ] has received considerable attention in the literature.

PSSs have been introduced in different contexts, such as family PSWs [ 15 - 19 ], the forensic field [ 20 , 21 ], and online PSSs. A considerable number of strategies were proposed to generate an effective PSS in the mental health field amid a number of associated concerns and challenges [ 22 - 25 ].

This general review sheds light on PSWs’ experiences, benefits, challenges, opportunities to expand access to quality addiction, and mental health care using PSSs. The review was conducted through a general search of the literature on MEDLINE, Google Scholar, EMBASE, Scopus, Chemical Abstracts, and PsycINFO. Search terms included peer support, peer support in mental health, social support, peer, family support, and integrated care. We began the review with an examination of the definitions, origins, and types of peer support contributions and within different clinical contexts, aiming at deepening the view to the diverse effects of such a workforce. We then continued with examining the transition from a patient role to a PSW role and their incorporation into mental health systems. Thereafter, we provided a conceptual framework for the effects of peer support and stigma in relation to PSWs. We concluded the review by examining the benefits and challenges associated with PSSs and provided a commentary on future directions for PSSs in mental health.

Definitions

Peer support has diverse meanings in the literature. For example, it is a system of giving and receiving help founded on key principles of respect, shared responsibility, and an agreement of what is helpful [ 26 ]. A peer is defined as an equal , someone with whom one shares demographic or social similarities, whereas support refers to “the kind of deeply felt empathy, encouragement, and assistance that people with shared experiences can offer one another within a reciprocal relationship” [ 3 ]. The Mental Health Foundation in the United Kingdom defined peer support in mental health as “the help and support that people with lived experience of a mental illness or a learning disability can give to one another” [ 27 ]. Peer employees were also defined as “individuals who fill designated unique peer positions as well as peers who are hired into traditional MH positions” [ 28 ]. In 1976, authors defined self-help groups as “voluntary small group structures for mutual aid in the accomplishment of a specific purpose...usually formed by peers who have come together for mutual assistance in satisfying a common need, overcoming a common handicap or life-disrupting problem, and bringing about desired social and/or personal change” [ 28 ]. Although the mutual relationship was sometimes overlooked and rather described as an asymmetric or nearly one-directional relationship [ 29 ], it is emphasized upon as 1 of the 4 main tasks for peer support accomplishments, which are mutuality, connection, worldwide, and moving toward rather than moving away [ 30 ].

Origin and Growth of Peer Support

Davidson et al [ 11 ] have expressed the paradigm that calls for new models of community-based practice, which turned away from case management and from conceptualizing old practices under new terms. In the 1990s, peer support was formally introduced as a service in community mental health care. However, there is evidence of its practice throughout history, including during the moral treatment era in France at the end of the 18th century [ 1 ]. Recently, peer support has been rapidly growing in many countries and could attract a considerable amount of research [ 22 ]. Although Lunatic Friends’ Society is known as the earliest peer support group in mental health, which was founded in England in the middle of the 19th century [ 31 ], self-help groups were described as the oldest and most pervasive of peer support types [ 28 ]. Some peer-run groups also formed in Germany in the late 19th century, which protested on involuntary confinement laws. In addition to this, several individuals in the 18th and 19th centuries publicized their protests about their treatment in autobiographies and petitions [ 32 ]. The origin of peer support even reaches further back than the earliest asylums [ 33 ]. Some authors suggest that peer support is not based on psychiatric models and diagnostic criteria [ 3 ]; however, it is about “understanding another’s situation empathically through the shared experience of emotional and psychological pain” [ 34 ]. In the United States, the start of legitimacy for peer support was ignited in 2007 by considering the conditions under which PSSs could be reimbursed by Medicaid [ 35 ]. Although this reform was entailing a recovery model, which has been adopted by health care providers and stakeholders in many “English-speaking” countries, it was not the case in many other countries, in which this reform was yet to be well formulated [ 36 ].

Transformational Concepts in Peer Support Service

Intentional peer support: informal to formal peer support evolution.

Intentional peer support (IPS) is described as a philosophical descendant of the informal peer support of the ex-patients’ movement in the 1970s [ 3 ]. It depends on a way of communication that immerses the provider into the recipient experience by stepping back from one’s story and being eagerly open to others’ stories [ 30 ]. In the field of psychiatry, trauma is blamed for playing a pivotal role in the experience, diagnosis, and treatment, and peer support is described as the logical environment for disseminating trauma-informed care (TIC) or service, which enables building relationships based on mutuality, shared power, and respect [ 37 ]. In the same context, trauma-informed peer support usually begins with the main question, “What happened to you?” instead of “What is wrong with you?” [ 30 ]. TIC is an explanatory model that identifies PSWs sharing lived experiences, ensuring safety and functioning as an advocate, and a liaison to patient management plans, where empowerment and intervention models are strongly emphasized upon [ 38 , 39 ]. The shift from a traditional biomedical model to recovery-oriented practice is meant to perceive trauma as a coping mechanism rather than a pathology [ 38 , 40 ]. This clearly entails training of all service providers for better acknowledgment and comfort in dealing with trauma survivors , with an understanding of trauma as an expectation rather than an exception [ 41 ]. Although the TIC concept has evolved over the years, it still lacks guidance, training, staff knowledge, and governmental support, which are necessary to ensure successful policy implementation [ 40 ]. The role of PSWs also extended to support those at risk of trauma events because of the nature of their work, including child protection workers, who are at risk of posttraumatic stress disorder or anxiety disorder [ 42 ]. Although IPS grew from the informal practices of grassroots-initiated peer support, it differs from earlier approaches because it is a theoretically based, manualized approach with clear goals and a fidelity tool for practitioners [ 14 ]. It instead focuses on the nature and purpose of the peer support relationship and its attention to skill building to purposefully engage in peer support relationships that promote mutual healing and growth [ 3 ]. Transitioning from informal to formal roles provides not only well-formulated expectations of the role but also a better chance to identify the potential conflict of the PSWs’ mixed identity [ 43 ].

Research conducted on PSWs has been conceptualized throughout history [ 22 ]. Starting with feasibility studies, at the initial stage, it is followed by studies comparing peer staff with nonpeer staff and, finally, the studies that answer questions such as the following:

  • Do interventions provided by peers differ from those provided by nonpeers?
  • What makes peer support a unique form of service delivery?

If so, to the previous question, what are the active ingredients of these aspects of peer support, and what outcomes can they produce?

Studies that provide answers to the latter set of questions are expected to provide a deeper understanding of the philosophical underpinnings of the IPS concept for PSSs.

The Transformation From Patient to Peer Support Providers

The shift from being a service recipient to a service provider has been contributing as a driving force to restore fundamental human rights, especially among those with serious mental illnesses (SMIs) [ 22 ]. Telling the personal lived experience leads to a profound shift, from telling an “illness story” to a “recovery story” [ 4 ]. This involved an identity transformation from being perceived as a victim or a patient to a person fully engaged in life with various opportunities ahead [ 4 ]. This transition is seen as a gradual process and one that is supported by several other personal changes with expected challenges [ 44 ]. Moving a full circle to include PSWs as the service provider has been undertaken by mental health services to further exceed the transformational role, which was primarily the main aim of providing such a service [ 45 ]. A liminal identity was given for PSWs as laying between several roles, being service users, friends, and staff. Thus, the professionalism of the PSW role might not be a successful way to ensure individual well-being or to promote the peer support initiative [ 46 ]. Thus, successful transitioning from the patient to PSW role involves fundamental functional shifts achieved through overcoming multiple barriers at the personal, health system, and societal levels.

Effects of Peer Support Service in Different Contexts

Trained PSWs or mentors can use communication behaviors useful to different client groups. Many studies showed the effectiveness and feasibility of applying for peer support as follows:

Severe or Serious Mental Illness

Generally, the evidence for peer support interventions for people with SMIs has been described as moderate to limited with mixed intervention effects [ 2 , 47 ]. On the one hand, adding PSSs to intensive case management teams proved to improve activation in terms of knowledge, skills, confidence, and attitudes for managing health and treatment. Hence, patients become healthier, report better quality of life (QOL), engage in more health care practices, and report more treatment satisfaction [ 48 , 49 ]. On the other hand, a systematic review of randomized controlled trials (RCTs) involving adults with SMIs, while showing some evidence of positive effects on measures of hope, recovery, and empowerment at and beyond the end of the PSS intervention in this review, did not show any positive effects on hospitalization, satisfaction, or overall symptoms [ 10 ]. Similarly, a Cochrane systematic review of PSSs for people with schizophrenia found inconclusive results, with a high risk of bias in most of the studies and insufficient data to support or refute the PSS for this group [ 50 ].

Addiction and Drug Users

In recent years, peer recovery support services have become an accepted part of the treatment for substance use disorders, providing a more extensive array of services that are typically associated with the mutual supportive intervention [ 51 ]. This is in contrast to the use of peer support for SMIs where evidence is still developing. The Substance Abuse and Mental Health Services Administration (SAMHSA) defined peer recovery support for substance use disorders as “a set of nonclinical, peer-based activities that engage, educate, and support individuals so that they can make life changes that are necessary to recover from substance use disorders” [ 51 ]. Despite the long-term nature of substance abuse, immersion in peer support groups and activities and active engagement in the community are considered the 2 critical predictors of recovery for more than half the dependent substance users [ 52 ].

A number of trials studied the peer support effect on drug users, especially in the emergency department [ 53 , 54 ]. Another randomized trial found that a socially focused treatment can affect change in the patient’s social network and hence increase support for abstinence, for example, an increase of one nondrinking friend in the social network is translated into a 27% increase in the probability of reporting abstinence on 90% of days or more at all follow-up visits, which extended to 15 months [ 55 ].

Forensic Peer Support Service

The forensic peer system refers to the engagement of peer specialists who have histories of mental illness as well as criminal justice involvement and who are trained to help other patients sharing similar accounts [ 20 ]. As referred to by Davidson and Rowe [ 20 ], “Forensic Peer Specialists embody the potential for recovery for people who confront the dual stigmas associated with SMI and criminal justice system involvement.”

They offer day-to-day support for those released early from jail by accompanying them to initial probation meetings or treatment appointments and referring them to potential employers and landlords, helping people to negotiate and minimize continuing criminal sanctions and training professional staff on engaging consumers with criminal justice history [ 20 , 21 ]. PSWs with incarceration histories could successfully identify the liminal space in being supportive rather than providing support for the criminal offense, in contrast with the conventional methods that directly confront criminality [ 56 ]. In fact, having criminal history is the “critical component” for achieving recovery [ 56 ]. Multiple initiatives have been introduced to facilitate a reentry process for people recently released from incarceration, including Forensic Assertive Community Treatment, Assertive Community Treatment, Critical Time Intervention, and Women’s Initiative Supporting Health Transitions Clinic, through diverse community support groups involving PSWs [ 57 , 58 ].

A peer support program undertaken by older community volunteers was effective in improving general and physical health, social functioning, depression parameters, and social support satisfaction, especially in socially isolated, low-income older adults [ 59 ]. The Reclaiming Joy Peer Support intervention (a mental health intervention that pairs an older adult volunteer with a participant) has the potential for decreasing depression symptoms and improving QOL indicators for both anxiety and depression [ 60 ]. Engaging the community in health research could be of a high value in acknowledging their own health needs [ 61 ].

Youth and Adolescents

Peer support programs are mostly needed for university students, where challenges with loneliness and isolation are well recognized [ 62 ]. Hence, a need emerged for training peers to support their peer adolescents with the prospective challenges at this age [ 63 ]. Trained peer support students without necessarily having a lived experience were also examined in England [ 64 ]. The study included university students measuring the acceptability and impact of the volunteer peer support program through 6 weekly sessions. Students with lower mental well-being were more likely to complete the course, and an improvement in mental well-being was recorded for those who attended more frequently. Overall, peers remain to be an essential source of support for young people experiencing mental health and substance use problems [ 65 ].

Medically and Socially Disadvantaged Subgroups

A peer-led, medical self-management program intervention has been beneficial for medically and socially disadvantaged subgroups [ 60 ]. The Reclaiming Joy Peer Support intervention has the potential for increasing QOL and reducing depression in low-income older adults who have physical health conditions [ 60 ]. Similarly, for those who are “hardly reached,” it was indicated that the PSS provided is even more effective in these marginalized populations [ 66 ]. A Health and Recovery Peer program was delivered by mental health peer leaders for people with SMIs, resulting in an improvement in the physical health–related QOL parameters such as physical activity and medication adherence [ 49 ]. Peer-delivered and technology-supported interventions are feasible and acceptable and are associated with improvements in psychiatric, medical self-management skills, QOL, and empowerment of older adults with SMIs and or chronic health conditions [ 67 , 68 ].

Persons With Disabilities

The United Nations’ Convention on the Rights of Persons with Disabilities (CRPD) was adopted in 2007 and stated that “persons with disabilities should have equal recognition before the law and the right to exercise their legal capacity” [ 69 - 71 ]. Therefore, a positive emphasis upon the supported decision making and the fight against discrimination is evident through the convention. Nevertheless, these initiatives have been perceived as incomplete considering many challenges such as the community social status and ongoing perceived stigma of people with disabilities (PWDs) [ 70 , 72 ]. “Circle of support” is an elaborate example of an applicable peer support model for PWDs that has helped in decision making and facilitating communication [ 70 , 73 , 74 ]. This is clearly aligned with the paradigm shift from the biomedical to the socially supportive model of disability, which was provided by CRPD [ 70 ].

Peer Support for Families

Families may act either as sources of understanding and support or stigmatization through ignorance, prejudice, and discrimination, with subsequent negative impact [ 19 ]. In addition, the distress and burden associated with caring for a family member with mental illness are evident, where 29% to 60% endure significant psychological distress [ 17 ]. Family support can be financial or emotional; however, moral support was perceived as the substantial motivating factor for relatives who are ill [ 19 ]. In the last few decades, consistent and growing evidence that supports the inclusion of family members in the treatment and care of their misfortunate relatives has been developed. This has been mainly evident in the youth mental health system that urged the transformation change, which incorporates family members in the health care service provided to their youth [ 18 , 75 ]. Many PSWs have been engaged in family psychoeducation as family peers or parent partners, especially for those with the first episode of psychosis [ 76 ]. Although familial education is crucial and needs to be provided through different scales [ 19 ], an extensive matching of PSWs and the caregivers has not been perceived as a necessity to create a successful volunteer mentoring relationship [ 77 ]. Multiple initiatives have taken place all over the world. In India, a program titled “Saathi” was established for family members of residential and outpatient mental health service users that had dual goals of offering information and developing a peer support mechanism for family members of people with different mental health conditions [ 19 ]. In Melbourne, Australia, “Families Helping Families” was developed, where family PSWs are positioned in the service assessment area and in the inpatient unit to ensure early involvement [ 18 ]. An impressive peer support guide for parents of children or youth with mental health problems is provided by the Canadian Mental Health Association, British Colombia Division [ 15 ]. In Ontario, family matters programs are provided through provincial peer support programs [ 16 ].

The term “transforming mental health care” entails active involvement of families in orienting the mental health system toward recovery [ 78 ]. Family members are to have access to timely and accurate information that promotes learning, self-monitoring, and accountability [ 79 ]. The inclusion of family members as partners of the medical service is the new philosophy, with a subsequent shift from the concept of clinic-based practice to a community-based service approach [ 78 ].

Peer Support Service in Low- and Middle-Income Countries

Several initiatives took place in low- and middle-income countries, such as in rural Uganda, where a trained peer-led team provided 12 successful training sessions of perinatal service for a group of parents over a 6-month period, which resulted in better maternal well-being and child development, compared with another control group [ 80 ]. Similarly, successful community peer groups were conducted in rural India and Nepal, with high feasibility and effectiveness rates, and perceived as “potential alternative to health-worker-led interventions” [ 81 - 83 ]. In addition, adding counseling and social support groups entailing PSWs to the conventional medication treatment for patients with psychotic disorders was tried in a cohort study in Uganda; however, the results were not significantly different from those who received only medications [ 84 ]. This might be because of the underpowering of community services offered, compared with the robust medication regimens [ 85 ].

It is evident from the aforementioned information that there is mixed evidence on the effectiveness of PSW interventions in different contexts. For example, for patients with SMIs, systematic reviews suggest that there is some evidence of positive effects on measures of hope, recovery, and empowerment but no positive effects on hospitalization, satisfaction, or overall symptoms [ 10 ]. Similarly, for patients with addiction issues, although being involved in a peer network did not reduce social assistance for alcohol, they somewhat increased behavioral and attitudinal support for abstinence as well as involvement with Alcoholics Anonymous [ 55 ]. Furthermore, although many observational studies support the PSW role in the other contexts described above, there is a current dearth of literature involving RCTs and systematic reviews reporting on the effectiveness of PSWs in these specific contexts. Thus, there exist opportunities for conducting RCTs in the described contexts.

The Conceptual Framework for the Effects of Peer Support Service

The conceptual framework is based on empirical evidence, suggesting that the impact of PSWs reflects upon the recipients of such a service [ 4 , 76 , 86 - 90 ], the global health system [ 22 , 47 , 76 , 86 , 91 , 92 ], and the PSWs themselves [ 13 , 28 , 76 , 93 ], as shown in Figure 1 . The framework has, therefore, been developed by authors through a general review of the literature that examines the effects of PSSs on patients, health care systems, and also PSWs themselves so as to provide evidence-based material supporting all possible effects of PSW roles.

Supportive social relationships can have a dual opposing effect on individuals’ lives, either as a family member or as social and professional networks through sharing their disappointments and pains or their joy and successes [ 11 ]. Useful roles for PSSs are identified in many studies. For example, adding 3 peer specialists to a team of 10 intensive case managers provided better QOL with greater satisfaction [ 12 ], stigma reduction, and less health service utilization [ 89 , 91 ]. The economic impact of PSSs has been extensively studied in the literature, concluding cost containment for the health care system in terms of reduction of readmission rates, emergency visits, and fewer hospital stays, which altogether substantially exceed the cost of running a peer support program [ 92 ]. Moreover, PSWs are looked at as providers of a service at a cheaper cost compared with other health care providers [ 94 , 95 ]. For example, about US $23,000 is paid to PSWs in the United States compared with around US $100,000 for a nurse practitioner [ 96 ]. However, a PSS is not posited as a substitute for clinical services, rather it is perceived as an intrapersonal and social service that provides a dual role of effective service and with humanizing care and support [ 14 , 26 , 97 ]. This role extends to cover PSWs themselves, in terms of improved overall well-being and self-confidence, reframing identity, and enhancing responsibility either toward themselves or their peers [ 13 , 93 ].

mental health literature review example

Although PSWs can play a variety of tasks, managers who hire them may want to ensure that improving patient activation is included in their range of duties [ 48 ]. In 2 concurrent studies, a significant increase in QOL satisfaction, reduction of rehospitalization rates, and reduction in the number of hospital days were recorded when adding PSSs to usual care [ 22 , 98 ]. In another study engaging 31 peer providers in diverse mental health, agencies identified 5 broad domains of wellness, including foundational, emotional, growth and spiritual, social, and occupational wellness [ 4 ]. In a systematic literature review for people with SMIs, peer-navigator interventions and self-management were the most promising interventions [ 47 ]. PSWs’ effects are diversified through sharing in different contexts. For example, positive impacts on the physical health of their peers have been recorded [ 49 ]. Peer-based approaches have been used to deliver behavioral weight loss interventions [ 90 ]. For young students, structured peer support for depression may have benefits in improving students’ mental well-being [ 64 ]. In the case of crisis houses, greater satisfaction was achieved through a provided informal PSS [ 99 ]. Robust studies, therefore, recommend implementing peer support programs [ 10 , 18 ].

On the other hand, authors found that PSSs met moderate levels of evidence and that effectiveness varied across service types, for example, with “peers in existing clinical roles” was described as being less effective than the “peer staff added to traditional services” and “peer staff delivering structured curricula” [ 3 ]. Other reviews suggested that current evidence does not support recommendations or mandatory requirements from policy makers to offer programs for peer support [ 9 , 10 ].

Peer Support Workers’ Satisfaction and Challenges

PSWs experience different problems alongside their diverse job roles, including low pay, stigma, unclear work roles, alienation, struggling with skill deficits, lack of training opportunities, emotional stress in helping others, and, on top of that, maintaining their personal physical and mental health wellness [ 100 , 101 ]. Researchers found that PSWs experience discrimination and prejudice from nonpeer workers, in addition to the encountered difficulties of how to manage the transition from being a patient to a PSW. As a result, high attrition rates were noted among PSWs in mental health settings [ 102 , 103 ]. Peer job satisfaction is strongly dependent on several factors [ 100 , 104 , 105 ]. Role clarity and psychological empowerment, organizational culture, and working partnership with peers were the most significant predictors of PSW job satisfaction, while professional prejudice was not perceived as a significant predictor [ 106 , 107 ]. Other studies noted that the main problems were experiencing marginalization, lack of understanding, and a sense of exclusion [ 108 - 110 ]. Payment could also contribute to the amount of satisfaction of PSWs [ 76 ], as compensation helps through facilitation and engagement motivation [ 109 ]. Nevertheless, it seems that not the payment, which ranged from US $10 to US $20 per hour, but the lack of recognition and acknowledgment are the causes for job nonsatisfaction [ 104 ].

An interesting literature review grouped these challenges and barriers facing PSWs during fulfilling their assigned roles into 6 main categories: nature of the innovation, individual professional, service user, social context, organizational context, and economic and political contexts [ 111 ].

It is evident from the abovementioned information that the PSW role is challenged at multiple levels, including at the personal, societal, and organizational levels. These challenges have a direct bearing on PSW satisfaction, and the successful integration of the PSW role into the health care system depends to a great extent on how these challenges are overcome.

Novel Technology in Peer Support Service (Online and Telephone)

Online support groups are usually conducted through bulletin boards, emails, or live chatting software [ 28 ]. Online groups are familiar with people whose illnesses are similar to SMIs or affecting the body shape that have forced them to experience embarrassment and social stigmatization [ 23 , 24 ]. Therefore, they split from the social contexts and redirect toward novel ways of help, such as PSWs and online support groups, and web-based communities provided a suitable medium for people with SMIs by following and learning from their peers on the web, which positively helped them to fight against stigma, instilling hope and gaining insight and empowerment for better health control [ 25 ]. Increasingly, social media grew as a target for individuals with SMIs, such as schizophrenia, schizoaffective disorder, or bipolar disorder, seeking advice and supporting each other [ 112 - 114 ]. For someone with SMIs, the decision to reach out and connect with others typically occurs at a time of increased instability or when facing significant life challenges [ 115 ]. In a qualitative study, popular social media, such as YouTube, appeared useful for allowing people with SMIs to feel less alone, find hope, support each other, and share personal experiences and coping strategies with day-to-day challenges of living with mental illness through listening and posting comments [ 114 ]. Mobile phone–based peer support was found to be a feasible and acceptable way to the youngsters during their pregnancy as well as in the postpartum period [ 116 ]. In addition, when coupled with frequent face-to-face meetings with PSWs and with “text for support,” it could be of high value for patients with different mental illnesses [ 117 ]. Although online peer networks actively fight against discrimination and stigma, their accessibility to diverse patients’ sectors regarding their income and ethnicity is still questionable [ 25 ].

Future of Peer Support Services

Potential new roles, such as community health workers, peer whole health coaches, peer wellness coaches, and peer navigators, have been suggested for such a workforce [ 76 ]. They are described as an “ill-defined potential new layer of professionals” [ 118 ]. Through an initiative undertaken by SAMHSA via its “Bringing Recovery Supports to Scale Technical Assistance Center Strategy,” a successful identification of abilities and critical knowledge necessarily required for PSWs who provide help and support for those recovering from mental health and substance abuse was noted [ 76 ]. At present, peer support is seen as a growing paradigm in many countries, including the United Kingdom, Canada, New Zealand, France, and the Netherlands [ 103 , 119 ]. As an evolving culture, peer support has the opportunity to forge not just mental health system change but social change as well [ 37 ]. A novel peer support system termed “Edmonton peer support system” (EPSS) is currently being tested in a randomized controlled pilot trial [ 117 ]. In this study, investigators are evaluating the effectiveness of an innovative peer support program that incorporates leadership training, mentorship, recognition, and reward systems for PSWs, coupled with automated daily supportive text messaging, which has proven effectiveness in feasibility trials involving patients with depression and alcohol use disorders [ 120 , 121 ]. Previous studies have examined the effect of PSSs in different contexts, including outpatient departments [ 122 ], emergency departments [ 53 , 54 ], community mental health clinics [ 123 , 124 ], and inpatient sites [ 125 ]. On the contrary, the EPSS study focuses on patients who have been discharged from acute care hospitals. These patients are being randomized into 1 of the 4 main groups: enrollment in a peer support system, enrollment in a peer support system plus automated daily supportive and reminder text messages, enrollment in automated daily supportive and reminder text messages alone, or treatment as usual follow-up care. The research team hypothesizes that patients who are assigned to a peer support system plus automated daily supportive and reminder text messages will show the best outcome.

Organizations may facilitate peer support through their values, actions, and oversight [ 119 ] and through a robust supervision system with available educational access, which could be the adequate path for creating a positive and risk-free environment for PSWs throughout their complex workloads [ 126 ]. On the other hand, ethics committees play essential roles in the inclusion of PSWs in applied research studies by avoiding repetition of the work of other trusted agencies and considering the ethical validity of consent procedures for peer support interventions [ 127 ].

There is an internationally growing trend to adopt PSSs within addiction and mental health services, and despite the ongoing challenges, large sections of the current literature support the inclusion of the PSWs into the mental health care workforce. The literature suggests that the benefits of PSSs impact not only the recipients of mental health services but also extend to the PSWs and the whole health care system. Although the expected benefits of PSSs might be directly measured in terms of service utilization or patient improvement indicators, this could also extend to include wellness and empowerment for PSWs, who may still be fragile, vulnerable, and in need of ongoing acknowledgment and recognition. Thus, the potential for PSSs to be embedded into routine care and the opportunities for the development of innovative models of care for addiction and mental health patients such as the EPSS, which incorporates PSSs and supportive text messaging [ 117 ], are evidently a high valued priority. However, the feasibility and maintenance of a robust PSS in health care would only be possible through collaborative efforts and ongoing support and engagement from all health care practitioners, managers, and other stakeholders.

This literature review has several limitations. First, the review is not a systematic review or meta-analysis, and as such, there were no well-defined inclusion or exclusion criteria of studies, which potentially could lead to the exclusion of some essential related studies. Second, the search was conducted in English publications only. Consequently, there is a high probability of missing critical related publications published in non-English languages. Finally, as the review depended mainly on the available literature from the aforementioned sources, which showed marked variability in their design and covered diverse ideas under the central theme, the different weights for each idea throughout the review could be noted.

Acknowledgments

This work was supported by Douglas Harding Trust Fund and Alberta Health Services.

Conflicts of Interest

None declared.

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Abbreviations

Edited by J Torous; submitted 21.07.19; peer-reviewed by F Mahomed, K Machin; comments to author 27.07.19; revised version received 06.09.19; accepted 15.02.20; published 09.06.20

©Reham A Hameed Shalaby, Vincent I O Agyapong. Originally published in JMIR Mental Health (http://mental.jmir.org), 09.06.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Mental Health, is properly cited. The complete bibliographic information, a link to the original publication on http://mental.jmir.org/, as well as this copyright and license information must be included.

Recommendation 22 Literature Review Summary

  • Mental health literacy encompasses knowledge about mental health symptoms, interventions, and resources available, as well as positive attitudes and willingness to intervene when others are struggling.
  • Literacy campaigns targeted at mental health have been positively received in post-secondary institutions, though it is unclear how they might affect behavioural outcomes.
  • Mental health training can improve knowledge, attitudes and self-efficacy. However, improvements often diminish over time, and it is unclear how actual gatekeeping behaviours are affected.
  • Barriers to participating in training programs include lack of awareness, time constraints, resource limitations, and uncertainty about the benefits of training.

Literature Review Findings

Mental health literacy is broadly defined as knowledge of mental health symptoms, interventions, and resources available, as well as positive attitudes and self-efficacy toward helping others in need. Many students were aware of counselling services and symptoms related to depression, but fewer recognized other campus resources and types of mental health conditions. Health promotion and prevention of mental health issues were under-recognized; students only endorsed help-seeking actions when symptoms were severe. Additionally, students experiencing high levels of depression and distress were less likely to recognize symptoms of mental illness than others.

Various mental health literacy campaigns have been implemented in post-secondary settings. Feedback collected through focus groups and surveys tended to be positive, though response rates were often low and outcomes following exposure were minimal. Campaigns utilizing visual promotion materials are more effective when they are designed appealingly and with a student audience in mind. There is also a need for campaigns targeted at groups at higher risk of experiencing mental distress, such as LGBTQ+ and racialized student groups.

Mental health training programs are associated with short-term increases in self-reported knowledge, attitudes, and self-efficacy. However, there is mixed evidence supporting changes to actual behaviours; (quasi-)experimental studies found few differences in skills following training. Training programs that included components such as experiential learning exercises and scenarios tailored to post-secondary settings were the most effective at improving outcomes. Limitations of studies on training programs include low participation and response rates, lack of long-term follow-up assessments, and the use of instruments that have not been empirically validated.  

Faculty, staff and students described barriers to participating in training programs, such as lack of awareness about training opportunities, limited time and resources, and uncertainty about the benefits of training given the role of the person. Support from peers and leaders in the community was a strong enabling factor for participating in training.

Implications for Practice 

Mental health literacy campaigns need to be embedded into a larger policy and service framework that emphasizes health promotion and prevention as well as intervention and crisis management. Tailored campaigns for high risk groups, such as minority student populations and those experiencing high levels of mental distress, are recommended.

As part of a mental health literacy strategy, training programs need to be available to all members of the university community. Training programs that are specialized for post-secondary settings, incorporate experiential exercises, and which receive institutional resources and ongoing support, are likely to have the most impact.

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  • Published: 28 January 2021

Evidence for implementation of interventions to promote mental health in the workplace: a systematic scoping review protocol

  • Charlotte Paterson   ORCID: orcid.org/0000-0001-6796-227X 1 ,
  • Caleb Leduc 2 , 3 ,
  • Margaret Maxwell 1 ,
  • Birgit Aust 4 ,
  • Benedikt L. Amann 5 ,
  • Arlinda Cerga-Pashoja 6 ,
  • Evelien Coppens 7 ,
  • Chrisje Couwenbergh 8 ,
  • Cliodhna O’Connor 2 , 3 ,
  • Ella Arensman 2 , 3 , 9 , 10 &
  • Birgit A. Greiner 2  

Systematic Reviews volume  10 , Article number:  41 ( 2021 ) Cite this article

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Mental health problems are common in the working population and represent a growing concern internationally, with potential impacts on workers, organisations, workplace health and compensation authorities, labour markets and social policies. Workplace interventions that create workplaces supportive of mental health, promote mental health awareness, destigmatise mental illness and support those with mental disorders are likely to improve health and economical outcomes for employees and organisations. Identifying factors associated with successful implementation of these interventions can improve intervention quality and evaluation, and facilitate the uptake and expansion. Therefore, we aim to review research reporting on the implementation of mental health promotion interventions delivered in workplace settings, in order to increase understanding of factors influencing successful delivery.

Methods and analysis

A scoping review will be conducted incorporating a stepwise methodology to identify relevant literature reviews, primary research and grey literature. This review is registered with Research Registry (reviewregistry897). One reviewer will conduct the search to identify English language studies in the following electronic databases from 2008 through to July 1, 2020: Scopus, PROSPERO, Health Technology Assessments, PubMed, Campbell Collaboration, Joanna Briggs Library, PsycINFO, Web of Science Core Collection, CINAHL and Institute of Occupational Safety and Health (IOSH). Reference searching, Google Scholar, Grey Matters, IOSH and expert contacts will be used to identify grey literature. Two reviewers will screen title and abstracts, aiming for 95% agreement, and then independently screen full texts for inclusion. Two reviewers will assess methodological quality of included studies using the Mixed Methods Appraisal Tool and extract and synthesize data in line with the RE-AIM framework, Nielson and Randall’s model of organisational-level interventions and Moore’s sustainability criteria, if the data allows. We will recruit and consult with international experts in the field to ensure engagement, reach and relevance of the main findings.

This will be the first systematic scoping review to identify and synthesise evidence of barriers and facilitators to implementing mental health promotion interventions in workplace settings. Our results will inform future evaluation studies and randomised controlled trials and highlight gaps in the evidence base.

Systematic review registration

Research Registry ( reviewregistry897 )

Peer Review reports

Mental health problems are common in the working population and represent a growing concern, with potential impacts on workers’ wellbeing, health and discrimination; organisations through lost productivity; workplace health and compensation authorities due to growing job stress-related claims; and social welfare systems owing to increased working age disability pensions for mental disorders [ 1 ]. Mental health refers to ‘a state of wellbeing in which the individual realizes his or her own abilities, can cope with normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community’ [ 2 ]. Mental health problems therefore include daily worries, stress, burnout and poor wellbeing, as well as mental health conditions such as depression or anxiety [ 3 ]. Psychosocial stresses in the workplace, such as job uncertainty, low job control, poor management, harassment and bullying, poor communication and long hours, have been shown to undermine mental wellbeing [ 4 ]. A negative working environment may lead to physical and mental health problems, harmful use of substances or alcohol, absenteeism, presenteeism and lost productivity [ 5 ]. Although it is acknowledged that mental health problems exist in the workplace, stigma and the social exclusion of people with mental health problems may be leading to under-recognition of such problems and the subsequent low treatment rate of mental health problems [ 6 , 7 , 8 ]. Under-treatment has been shown to increase the indirect cost of mental disorders, physical morbidity and mortality [ 9 , 10 ].

Several studies have evaluated workplace interventions targeting mental wellbeing [ 11 ]. Workplace interventions that support mental health and wellbeing have been shown to help reduce sickness absence [ 12 ]. In addition, workplaces that promote mental health awareness, destigmatise mental illness and support people with mental disorders are more likely to reduce levels of depression and absenteeism while increasing productivity as well as benefiting from associated economic gains [ 13 ]. Improving access to evidence-based interventions for minor stress-related depressive symptoms in occupational sectors associated with high suicide rates, e.g. construction, healthcare and information communication and technology (ICT), is likely to prevent the development of severe depressive disorders and comorbidities, and subsequent suicidal behaviour [ 13 ].

Although high-quality evaluations underpin evidence-based interventions (EBI), implementation research can improve the quality of such evaluations and facilitate the uptake and reach of EBIs and other research findings into practice [ 14 ]. One effective way to do this is to identify factors that influence the delivery and uptake of interventions during development, feasibility, evaluation and implementation stages [ 15 ].

So far, research into specific mechanisms and process factors associated with the successful delivery of mental health promotion interventions in the workplace is limited [ 16 , 17 ]. This review aims to identify and analyse research on the implementation of workplace mental health promotion interventions; specifically, to understand the barriers and facilitators that influence their delivery in order to provide insights and inform future intervention, evaluation and implementation efforts. This work represents a direct response to recent calls within intervention research to examine the mechanisms through which interventions bring about change and the documentation of contextual and procedural considerations that either facilitate or limit implementation [ 16 , 17 ].

Aims and objectives

This review is part of a wider project intending to develop, evaluate and implement a multi-level intervention (Mental Health Promotion and Intervention in Occupational Settings, MENTUPP) [ 18 ], which aims to improve mental health and wellbeing in the workplace involving 15 European and Australian partners, with a particular focus on small to medium sized enterprises (SMEs) in three sectors with high prevalence rates of mental health problems and suicidal behaviour, namely ICT, healthcare and construction sectors. More broadly, the purpose of this review is to collate and critically appraise workplace mental health intervention implementation literature to understand how and why certain interventions are more effectively implemented than others and inform MENTUPP and future programmes. The objectives of the review are to:

1. Systematically identify and document research explicitly reporting on the quality of delivery and implementation of mental health promotion interventions in workplaces (e.g. reporting the quality of implementation, a process evaluation or realist evaluation) and, if the evidence allows, specifically in ICT, construction and healthcare settings and SMEs.

2. Identify the barriers and facilitators associated with the quality of implementation of mental health promotion interventions in workplace settings and, if the evidence allows, specifically in ICT, construction or healthcare settings and within SMEs, as it relates to the MENTUPP programme of work.

Based on these objectives, our research questions are:

What is the scope of research with explicit analysis of implementation aspects of mental health promotion interventions in the workplace?

What are the barriers and facilitators to implementing mental health promotion interventions in the workplace?

What are the barriers and facilitators to implementing mental health promotion interventions in SMEs and in the ICT, construction and healthcare sectors?

Methods/design

Study design.

We will conduct a systematic scoping review using the 6-stage scoping review framework [ 19 , 20 ] to systematically identify the implementation evidence and factors associated with successful implementation of mental health promotion in workplace settings. Scoping reviews aim to map a broad field of literature and to summarise and disseminate research findings [ 19 , 21 ], rather than address very focussed questions. This approach is in line with the aims of this review, given the wide range of potential successful and failed interventions, contexts and implementation factors. We will comprehensively explore the relevant research, using iterative methods to develop a rigorous and systematic search of the existing literature [ 20 ]. We will recruit and consult with international experts in the field according to both applied organisational and research experience at key stages of the review process and subsequently to ensure engagement, reach and relevance of the process and main findings. The active involvement of people affected by a research topic has been argued to be beneficial to the quality, relevance and impact of research [ 22 , 23 ], and it enhances the perceived usefulness of systematic review evidence and addresses barriers to the uptake of synthesised research evidence [ 24 , 25 ].

Our protocol was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Protocol checklist (PRISMA-P) [ 26 ] (see Additional file 1 ). The present protocol has been registered within the Research Registry (reviewregistry897). The results of our scoping review will be reported in accordance with PRISMA-ScR [ 27 ].

Operationally, the current review will systematically conduct the searches based on the following definition of key terms :

● Implementation : The results of this review will inform the design of a feasibility and definitive trial of mental health promotion in the workplace. As such, implementation refers to interventions being delivered at feasibility and piloting, evaluation and implementation stages of the Medical Research Council (MRC) framework (15).

● Mental health promotion refers to interventions or programmes that aim to treat (intervene to improve mental health), prevent (inhibit the escalation of subclinical symptoms to clinical severity or prevent the onset of mental health problems) and promote (improve mental health by targeting positive components of mental health) mental health and wellbeing [ 28 ].

● Barriers are defined as any variable or condition that impedes the implementation or delivery of mental health promotion interventions.

● Facilitators are defined as any variable or condition that facilitates or improves the implementation or delivery of mental health promotion interventions.

● Workplace settings include any organisation operating with paid employees. Therefore, mental health promotion interventions must be delivered through, or be associated with, the workplace. Sector-specific definitions from the European Commission were used [ 29 ]. The ICT sector will include telecommunications activities, information technology activities and other information service activities (divisions 61–63); the healthcare sector will include healthcare provided by medical professionals in hospitals or other facilities and residential activities, but not social work activities (divisions 86–87); and the construction sector will include construction of buildings, civil engineering and specialised construction activities (divisions 41–43). Small- to medium-sized enterprises include those employing < 250 employees [ 30 ].

Information sources and search strategy

We will use iterative methods to develop and apply a rigorous and comprehensive search strategy, combining a series of free text terms and Medical Subject Headings (MeSH) terms for key concepts: (a) workplace AND (b) mental health, AND (c) interventions, AND (d) implementation. A preliminary search strategy (see Additional file 2 ) has been developed for PsycINFO, using established search terms (from Cochrane and other previous search strategies [ 31 , 32 , 33 ], peer-reviewed in accordance with PRESS guidelines [ 34 ]. Boolean operators will be used to maximise the penetration of terms searched, and appropriate “wild cards” will be employed to account for plurals, variations in databases, and spelling.

We will use a stepwise methodology [ 35 ] to identify the highest quality evidence in a systematic way and capture grey literature. Grey literature will be included because it is likely that due to publication bias some unsuccessful interventions have not been published in peer-reviewed journals. A number of contingency plans have been built into the methods to allow an iterative approach to the search and selection of evidence for the review (Additional file 3 ). We will use established search terms and adapt searches for each of the following major electronic databases outlined below.

In step 1, we will search the following electronic databases for systematic reviews:

● Health Technology Assessments

● Campbell Collaboration

● Joanna Briggs Library

● Web of Science Core Collection

In step 2, we will look for primary studies reporting implementation of mental health promotion interventions in the following electronic databases:

● PsychINFO

● Institute of Occupational Safety and Health (IOSH) research database.

Step 3 will involve supplementary searches involving a thorough review of relevant study references, grey literature and personal contacts using a systematic approach (Additional file 3 ). This will include searching:

● Reference searching : relevant studies included in published guidelines, relevant systematic reviews and listed in the included studies’ reference lists and bibliographies.

● Grey literature : Google Scholar (25 pages relevant), Grey Matters and the Institute of Occupational Safety and Health (IOSH) research database.

● Personal contacts : we will contact international experts and authors of papers reporting trials (from 2008) on workplace interventions to address mental health promotion.

Criteria for considering studies for inclusion

The scoping review will address factors associated with successful implementation and therefore focus primarily on feasibility and process studies or realist evaluations. Although we will look at the relation between implementation and effects, the main aim of the review is to identify factors associated with implementation, specifically barriers and facilitators. The focus of this review will be cognisant of outcomes indicating successful implementation, including programme uptake, retention and impact.

Study designs

We will include any paper, regardless of study design, using either quantitative, qualitative or mixed-methods, which explicitly investigates, reports or discusses, in the title or abstract, any aspect of implementation of specific mental health promotion interventions (i.e. quality of implementation, a process evaluation including rich data or a realist evaluation) delivered in the workplace. This includes literature reviews (systematic reviews, scoping reviews, meta-analyses) and primary research studies published either in the peer-reviewed scientific literature or in the grey literature. We will exclude opinion pieces, commentaries, website discussions, blogs and magazine and newspaper articles.

We will include studies with adult participants (aged 16–65) who are in formal employment, including those on sickness absence leave and are expected to return to work.

Interventions

Interventions, whose implementation is of interest, are purposefully applied strategies delivered in the workplace, targeting either workers, supervisors, managers, occupational health professionals, owners/executives or entire organisations. Included interventions will aim to (i) help protect mental health by reducing work-related risk factors (e.g. job strain, poor working conditions and job stressors such as job insecurity, psychological harassment (e.g. due to stigma), low social support at work, organisational injustice, and effort-reward imbalance); (ii) promote workplace mental health wellbeing by creating positive aspects of work, and develop employees’ strengths (e.g. satisfaction, wellbeing, psychological capital, positive mental health, resilience and positive organisational attributes such as authentic leadership, supportive workplace culture and workplace social capital); and (iii) respond to mental health problems when they occur (e.g. interventions targeting burnout, stress, anxiety, depression or return to work) [ 36 ]. We will exclude studies that evaluate the implementation of general mental health interventions that are not specifically associated with workplace factors or delivered in work contexts (e.g. healthy eating or exercise at home), mental health interventions that are not formally implemented in the workplace (e.g. online work-related mental health interventions freely available online without association to an organisation) and one-off events (e.g. distribution of mental health educational material or one-off information sessions through guest lecturers). Interventions not directly targeting psychological wellbeing or mental health will be included if the primary outcome is related to psychological wellbeing or mental health (e.g. a physical activity programmes delivered in the workplace with a primary outcome for improving mental health). Interventions that target a wide range of health and wellbeing outcomes, e.g. physical activity, obesity, smoking cessation and stress, will be excluded.

Outcomes of interest

We will only include studies reporting rich data on any implementation outcomes and will categorise outcomes within our data charting. We anticipate that identified outcomes may include fidelity, reach, dose delivered, dose received, adoption, penetration, feasibility, acceptability, context factors, process factors, sustainability factors, programme theories, theories of change and failure theories. We will exclude studies focusing on only the impact of interventions on disease end points, i.e. which do not evaluate implementation quality.

Types of settings

We will include studies conducted in any geographical location, and we will categorise the location based on relevance to Europe and Australia during data charting. The intervention must be delivered in, or in association with, a workplace setting and be implemented in the work schedule, work systems or administrative structures.

Studies published in English will be included in steps 1 and 2. Studies published in English, French and German will be included in step 3.

Publication date

Studies published in the last 13 years will be included. The World Health Organization’s (WHO) Global Plan of Action on Work’s Health (2008–2017) [ 37 ] and the Mental Health Action Plan (2013–2020) [ 38 ] highlight the importance of promoting good mental health in the workplace. Furthermore, the field of implementation science is fairly new; therefore, literature published after 2008 is deemed to be most relevant to this review.

Study selection

Rayyan will be used for the study selection process [ 39 ]. Two reviewers will be utilised for a provisional screening of all titles (CP, CL), removing any clearly irrelevant papers. To ensure reliability between reviewers, 15% of the study titles will be reviewed blindly by both reviewers independently, aiming for 95% agreement. Where 95% agreement is not reached, a further 15% will be reviewed by both reviewers independently. Any discrepancy between reviewers will be discussed and, if necessary, will involve a third reviewer to resolve. The remaining study titles will be screened for abstract review by a single reviewer. Two reviewers will then be involved in screening the remaining potential abstracts (CP, CL) and rate them as relevant, irrelevant or unsure. To ensure consistency between reviewers, 15% will be checked independently, and where agreement does not reach 95%, a further 15% will be reviewed by both reviewers. Studies that are ranked as irrelevant will be excluded. We will obtain the full papers for the remaining studies. Two reviewers (CP, CL) will then independently assess each of these against the selection criteria. We will resolve any disagreement through discussion and will involve a third independent reviewer if needed.

Charting the data

Data extraction.

We will pilot a data extraction template on the first four included studies and amend as required. We will extract key study details (e.g. study design, country, sample size, sector, intervention characteristics, impact on primary outcome, etc.) and implementation data (e.g. direct quotes, page numbers) will be structured using an adapted version of the RE-AIM framework [ 40 ] which has been complemented using selected categories from Nielson and Randall’s model of organisational-level interventions [ 16 ] and Moore’s sustainability criteria [ 41 ]. To ensure reliability, data from 15% of included papers will be coded by two reviewers (CP and CL) independently. Any ambiguity identified will be resolved through discussion with other members of the review team. Study authors will be contacted via email where data are missing or unclearly reported.

Data coding

Data will be coded as follows:

● Stage of intervention development/evaluation will be coded according to the MRC framework (i.e. feasibility, evaluation or implementation) [ 15 ].

● Countries will be coded using the World Bank classification [ 42 ] to identify countries of relevance to future research, e.g. Europe and Australia.

● Implementation evidence will be mapped using a modified version of the RE-AIM framework [ 40 ], which is organised into five categories: reach, effectiveness, adoption, implementation and maintenance. This framework also allows evaluation of implementation at an individual and organisational level.

● Nielson and Randall’s model of organisational-level interventions [ 16 ] will supplement the RE-AIM framework for this review allowing for extraction based on the intervention itself, the context in which it was delivered and participants’ mental models.

● Intervention sustainability will be coded using Moore’s definitions of sustainability [ 41 ], e.g. continued delivery, behaviour change, evolution/adaptation and continued benefits.

Quality appraisal

In line with previous systematic and scoping reviews that include mixed methods literature [ 32 , 43 ], the methodological quality of included studies will be assessed using the Mixed Methods Appraisal Tool (MMAT) [ 44 ] for quantitative, qualitative and mixed methods research designs. Each study will receive a methodological rating between 0 and 100 (with 100 being the highest quality), based on the evaluation of study selection bias, study design, data collection methods, sample size, intervention integrity and analysis. Where studies integrate the process evaluation into the study design, the quality of the entire study will be assessed. Methodological quality will be rated by two reviewers (CL and CP). To ensure consistency between reviewers, 15% will be rated independently, and if agreement is reached, one reviewer will rate the remaining papers. Any ambiguity identified will be resolved through discussion with other members of the review team.

Collating, summarising and reporting

Descriptive characteristics of included studies will be tabulated and brought together using a narrative synthesis. To answer question one, we will summarise the type of evidence relating to the implementation of the interventions in workplace settings. To answer questions two and three, barriers and facilitators will be categorised according to the RE-AIM framework [ 40 ], modified using Nielson & Randall’s (2013) model for evaluation organisational-level interventions [ 16 ] and Moore’s sustainability criteria [ 45 ]. We will present tabulated data by sector and then occupational level (i.e. organisational, managerial, etc.) and intervention type. If the evidence allows, to further answer research question three, we will present tabulated data from included studies focusing specifically on SMEs using the same format. Key findings will be brought together within a narrative synthesis [ 46 , 47 ].

The aim of this systematic scoping review is to identify research that reports on the feasibility and implementation of mental health promotion interventions that are delivered in workplace settings, and to specifically understand the factors (barriers and facilitators) that influence the successful delivery of mental health promotion interventions in the workplace. This review is part of the MENTUPP project [ 18 ] which aims to develop, evaluate and implement mental health promotion interventions for the workplace, particularly in SMEs in the construction, healthcare and ICT sectors. As such, our review will aim to focus on intervention implementation barriers and facilitators in SMEs and in the construction, healthcare and ICT sectors. This work addresses recent calls within intervention research to examine the mechanisms through which interventions bring about change and the documentation of contextual and procedural considerations that either facilitate or limit implementation [ 16 , 17 ]. Additionally, this timely review responds to international policy regarding mental health in the workplace [ 8 ]. In an effort to maintain quality and identify all relevant information, we have presented a rigorous and systematic approach to this scoping review. We have maintained a broad search strategy in order to capture the variety of implementation research that may be available, and we will consult with stakeholders to ensure the main findings are useful and relevant. The results of this review will identify barriers and facilitators to implementation of mental health promotion interventions in the workplace and inform future pilot and definitive RCTs within the MENTUPP project [ 18 ]. This will help inform future interventions, and the evaluation and implementation efforts of such interventions, which will subsequently improve outcomes for employees and organisations through improved mental wellbeing; reduced symptoms of depression, anxiety and stress; and reduced presenteeism and absenteeism. In addition, this review will contribute to implementation science related to workplace mental health promotion.

Availability of data and materials

All data generated or analysed during this study will be included in the published scoping review article and will be available by request to the corresponding author.

Abbreviations

Information and communication technology

Institution of occupational safety and health

Medical research council

Medical subject heading

Mixed methods appraisal tool

Preferred reporting items for systematic reviews and meta-analyses

Preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews

Randomized controlled trial

Reach, effectiveness, adoption, implementation and maintenance

Small-to-medium sized enterprises

World health Organization

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Acknowledgements

The authors express their gratitude to Pauline Campbell, Glasgow Caledonian University, for her guidance and support with the search strategy, and to Donna O’Doibhlin, University College Cork, for her agreement to review the final search strategy. The authors would also like to express appreciation to all other MENTUPP partners [ 18 ].

This study is funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 848137. The material presented and views expressed here are the responsibility of the author(s) only. The EU Commission takes no responsibility for any use made of the information set out.

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Charlotte Paterson & Margaret Maxwell

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Caleb Leduc, Cliodhna O’Connor, Ella Arensman & Birgit A. Greiner

National Suicide Research Foundation, Cork, Ireland

Caleb Leduc, Cliodhna O’Connor & Ella Arensman

National Research Centre for the Working Environment, Copenhagen, Denmark

Birgit Aust

Centro Forum Research Unit, Institut de Neuropsiquiatria i Addicions (INAD), Parc de Salut Mar, Hospital del Mar Medical Research Institute (IMIM), Autonomous University Barcelona, CIBERSAM, Barcelona, Spain

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London School of Hygiene and Tropical Medicine, London, UK

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Centre for Health Research and Consultancy, Katholieke Universiteit Leuven, Leuven, Belgium

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Australian Institute for Research, Griffith University, Mount Gravatt, Australia

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The protocol was conceptualised, designed, reviewed and approved by all authors. MM and CP contributed to the writing of the protocol. The subsequent study, review of abstracts, full studies and synthesis will be conducted by CP and CL and supported by MM, BG and BA.

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PRISMA-P checklist.

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Draft Search Strategy.

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Outline of the step-wise review methodology.

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Paterson, C., Leduc, C., Maxwell, M. et al. Evidence for implementation of interventions to promote mental health in the workplace: a systematic scoping review protocol. Syst Rev 10 , 41 (2021). https://doi.org/10.1186/s13643-020-01570-9

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  • Barriers and facilitators
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Mental Health Screening: Recommendations from an Integrated Literature Review

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School-based, multiple gate mental health screening has been identified as a major component of social, emotional, and behavioral systems of support models, and a promising practice that can be used to address unmet mental health needs of children and adolescents. To better inform implementation of multiple gate screening programs, we completed an integrated literature review based on a review of 38 school-based screening studies identified through a systematic review process. The focus of the review was on effective and ineffective screening strategies – and general screening considerations – presented in the identified studies. Considerations and implementation strategies related to mental health screening in schools, including issues related to consent and student participation, screening measures, how to integrate screening into school programs, how to manage suicide risk screening, and how to support students after screening are discussed. Considerations for future research are also discussed. Multiple-stage school-based mental health screening can improve identification of mental health needs and access to mental health services. However, special consideration must be given to implementing screening, and additional areas of research are needed to further our knowledge and practice recommendations in this area.

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1. Introduction

Not to be confused with a book review, a  literature review  surveys scholarly articles, books and other sources (e.g. dissertations, conference proceedings) relevant to a particular issue, area of research, or theory, providing a description, summary, and critical evaluation of each work. The purpose is to offer an overview of significant literature published on a topic.

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  • Literature search—finding materials relevant to the subject being explored
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  • Division of works under review into categories (e.g. those in support of a particular position, those against, and those offering alternative theses entirely)
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The effectiveness of virtual reality training on knowledge, skills and attitudes of health care professionals and students in assessing and treating mental health disorders: a systematic review

  • Cathrine W. Steen 1 , 2 ,
  • Kerstin Söderström 1 , 2 ,
  • Bjørn Stensrud 3 ,
  • Inger Beate Nylund 2 &
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Virtual reality (VR) training can enhance health professionals’ learning. However, there are ambiguous findings on the effectiveness of VR as an educational tool in mental health. We therefore reviewed the existing literature on the effectiveness of VR training on health professionals’ knowledge, skills, and attitudes in assessing and treating patients with mental health disorders.

We searched MEDLINE, PsycINFO (via Ovid), the Cochrane Library, ERIC, CINAHL (on EBSCOhost), Web of Science Core Collection, and the Scopus database for studies published from January 1985 to July 2023. We included all studies evaluating the effect of VR training interventions on attitudes, knowledge, and skills pertinent to the assessment and treatment of mental health disorders and published in English or Scandinavian languages. The quality of the evidence in randomized controlled trials was assessed with the Cochrane Risk of Bias Tool 2.0. For non-randomized studies, we assessed the quality of the studies with the ROBINS-I tool.

Of 4170 unique records identified, eight studies were eligible. The four randomized controlled trials were assessed as having some concern or a high risk of overall bias. The four non-randomized studies were assessed as having a moderate to serious overall risk of bias. Of the eight included studies, four used a virtual standardized patient design to simulate training situations, two studies used interactive patient scenario training designs, while two studies used a virtual patient game design. The results suggest that VR training interventions can promote knowledge and skills acquisition.

Conclusions

The findings indicate that VR interventions can effectively train health care personnel to acquire knowledge and skills in the assessment and treatment of mental health disorders. However, study heterogeneity, prevalence of small sample sizes, and many studies with a high or serious risk of bias suggest an uncertain evidence base. Future research on the effectiveness of VR training should include assessment of immersive VR training designs and a focus on more robust studies with larger sample sizes.

Trial registration

This review was pre-registered in the Open Science Framework register with the ID-number Z8EDK.

Peer Review reports

A robustly trained health care workforce is pivotal to forging a resilient health care system [ 1 ], and there is an urgent need to develop innovative methods and emerging technologies for health care workforce education [ 2 ]. Virtual reality technology designs for clinical training have emerged as a promising avenue for increasing the competence of health care professionals, reflecting their potential to provide effective training [ 3 ].

Virtual reality (VR) is a dynamic and diverse field, and can be described as a computer-generated environment that simulates sensory experiences, where user interactions play a role in shaping the course of events within that environment [ 4 ]. When optimally designed, VR gives users the feeling that they are physically within this simulated space, unlocking its potential as a dynamic and immersive learning tool [ 5 ]. The cornerstone of the allure of VR is its capacity for creating artificial settings via sensory deceptions, encapsulated by the term ‘immersion’. Immersion conveys the sensation of being deeply engrossed or enveloped in an alternate world, akin to absorption in a video game. Some VR systems will be more immersive than others, based on the technology used to influence the senses. However, the degree of immersion does not necessarily determine the user’s level of engagement with the application [ 6 ].

A common approach to categorizing VR systems is based on the design of the technology used, allowing them to be classified into: 1) non-immersive desktop systems, where users experience virtual environments through a computer screen, 2) immersive CAVE systems with large projected images and motion trackers to adjust the image to the user, and 3) fully immersive head-mounted display systems that involve users wearing a headset that fully covers their eyes and ears, thus entirely immersing them in the virtual environment [ 7 ]. Advances in VR technology have enabled a wide range of VR experiences. The possibility for health care professionals to repeatedly practice clinical skills with virtual patients in a risk-free environment offers an invaluable learning platform for health care education.

The impact of VR training on health care professionals’ learning has predominantly been researched in terms of the enhancement of technical surgical abilities. This includes refining procedural planning, familiarizing oneself with medical instruments, and practicing psychomotor skills such as dexterity, accuracy, and speed [ 8 , 9 ]. In contrast, the exploration of VR training in fostering non-technical or ‘soft’ skills, such as communication and teamwork, appears to be less prevalent [ 10 ]. A recent systematic review evaluates the outcomes of VR training in non-technical skills across various medical specialties [ 11 ], focusing on vital cognitive abilities (e.g., situation awareness, decision-making) and interprofessional social competencies (e.g., teamwork, conflict resolution, leadership). These skills are pivotal in promoting collaboration among colleagues and ensuring a safe health care environment. At the same time, they are not sufficiently comprehensive for encounters with patients with mental health disorders.

For health care professionals providing care to patients with mental health disorders, acquiring specific skills, knowledge, and empathic attitudes is of utmost importance. Many individuals experiencing mental health challenges may find it difficult to communicate their thoughts and feelings, and it is therefore essential for health care providers to cultivate an environment where patients feel safe and encouraged to share feelings and thoughts. Beyond fostering trust, health care professionals must also possess in-depth knowledge about the nature and treatment of various mental health disorders. Moreover, they must actively practice and internalize the skills necessary to translate their knowledge into clinical practice. While the conventional approach to training mental health clinical skills has been through simulation or role-playing with peers under expert supervision and practicing with real patients, the emergence of VR applications presents a compelling alternative. This technology promises a potentially transformative way to train mental health professionals. Our review identifies specific outcomes in knowledge, skills, and attitudes, covering areas from theoretical understanding to practical application and patient interaction. By focusing on these measurable concepts, which are in line with current healthcare education guidelines [ 12 ], we aim to contribute to the knowledge base and provide a detailed analysis of the complexities in mental health care training. This approach is designed to highlight the VR training’s practical relevance alongside its contribution to academic discourse.

A recent systematic review evaluated the effects of virtual patient (VP) interventions on knowledge, skills, and attitudes in undergraduate psychiatry education [ 13 ]. This review’s scope is limited to assessing VP interventions and does not cover other types of VR training interventions. Furthermore, it adopts a classification of VP different from our review, rendering their findings and conclusions not directly comparable to ours.

To the best of our knowledge, no systematic review has assessed and summarized the effectiveness of VR training interventions for health professionals in the assessment and treatment of mental health disorders. This systematic review addresses the gap by exploring the effectiveness of virtual reality in the training of knowledge, skills, and attitudes health professionals need to master in the assessment and treatment of mental health disorders.

This systematic review follows the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analysis [ 14 ]. The protocol of the systematic review was registered in the Open Science Framework register with the registration ID Z8EDK.

We included randomized controlled trials, cohort studies, and pretest–posttest studies, which met the following criteria: a) a population of health care professionals or health care professional students, b) assessed the effectiveness of a VR application in assessing and treating mental health disorders, and c) reported changes in knowledge, skills, or attitudes. We excluded studies evaluating VR interventions not designed for training in assessing and treating mental health disorders (e.g., training of surgical skills), studies evaluating VR training from the first-person perspective, studies that used VR interventions for non-educational purposes and studies where VR interventions trained patients with mental health problems (e.g., social skills training). We also excluded studies not published in English or Scandinavian languages.

Search strategy

The literature search reporting was guided by relevant items in PRISMA-S [ 15 ]. In collaboration with a senior academic librarian (IBN), we developed the search strategy for the systematic review. Inspired by the ‘pearl harvesting’ information retrieval approach [ 16 ], we anticipated a broad spectrum of terms related to our interdisciplinary query. Recognizing that various terminologies could encapsulate our central ideas, we harvested an array of terms for each of the four elements ‘health care professionals and health care students’, ‘VR’, ‘training’, and ‘mental health’. The pearl harvesting framework [ 16 ] consists of four steps which we followed with some minor adaptions. Step 1: We searched for and sampled a set of relevant research articles, a book chapter, and literature reviews. Step 2: The librarian scrutinized titles, abstracts, and author keywords, as well as subject headings used in databases, and collected relevant terms. Step 3: The librarian refined the lists of terms. Step 4: The review group, in collaboration with a VR consultant from KildeGruppen AS (a Norwegian media company), validated the refined lists of terms to ensure they included all relevant VR search terms. This process for the element VR resulted in the inclusion of search terms such as ‘3D simulated environment’, ‘second life simulation’, ‘virtual patient’, and ‘virtual world’. We were given a peer review of the search strategy by an academic librarian at Inland Norway University of Applied Sciences.

In June and July 2021, we performed comprehensive searches for publications dating from January 1985 to the present. This period for the inclusion of studies was chosen since VR systems designed for training in health care first emerged in the early 1990s. The searches were carried out in seven databases: MEDLINE and PsycInfo (on Ovid), ERIC and CINAHL (on EBSCOhost), the Cochrane Library, Web of Science Core Collection, and Scopus. Detailed search strategies from each database are available for public access at DataverseNO [ 17 ]. On July 2, 2021, a search in CINAHL yielded 993 hits. However, when attempting to transfer these records to EndNote using the ‘Folder View’—a feature designed for organizing and managing selected records before export—only 982 records were successfully transferred. This discrepancy indicates that 11 records could not be transferred through Folder View, for reasons not specified. The process was repeated twice, consistently yielding the same discrepancy. The missing 11 records pose a risk of failing to capture relevant studies in the initial search. In July 2023, to make sure that we included the latest publications, we updated our initial searches, focusing on entries since January 1, 2021. This ensured that we did not miss any new references recently added to these databases. Due to a lack of access to the Cochrane Library in July 2023, we used EBMR (Evidence Based Medicine Reviews) on the Ovid platform instead, including the databases Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Cochrane Clinical Answers. All references were exported to Endnote and duplicates were removed. The number of records from each database can be observed in the PRISMA diagram [ 14 ], Fig.  1 .

figure 1

PRISMA flow chart of the records and study selection process

Study selection and data collection

Two reviewers (JS, CWS) independently assessed the titles and abstracts of studies retrieved from the literature search based on the eligibility criteria. We employed the Rayyan website for the screening process [ 18 ]. The same reviewers (JS, CWS) assessed the full-text articles selected after the initial screening. Articles meeting the eligibility criteria were incorporated into the review. Any disagreements were resolved through discussion.

Data extracted from the studies by the first author (CWS) and cross-checked by another reviewer (JS) included: authors of the study, publication year, country, study design, participant details (education, setting), interventions (VR system, class label), comparison types, outcomes, and main findings. This data is summarized in Table  1 and Additional file 1 . In the process of reviewing the VR interventions utilized within the included studies, we sought expertise from advisers associated with VRINN, a Norwegian immersive learning cluster, and SIMInnlandet, a center dedicated to simulation in mental health care at Innlandet Hospital Trust. This collaboration ensured a thorough examination and accurate categorization of the VR technologies applied. Furthermore, the classification of the learning designs employed in the VP interventions was conducted under the guidance of an experienced VP scholar at Paracelcus Medical University in Salzburg.

Data analysis

We initially intended to perform a meta-analysis with knowledge, skills, and attitudes as primary outcomes, planning separate analyses for each. However, due to significant heterogeneity observed among the included studies, it was not feasible to carry out a meta-analysis. Consequently, we opted for a narrative synthesis based on these pre-determined outcomes of knowledge, skills, and attitudes. This approach allowed for an analysis of the relationships both within and between the studies. The effect sizes were calculated using a web-based effect size calculator [ 27 ]. We have interpreted effect sizes based on commonly used descriptions for Cohen’s d: small = 0.2, moderate = 0.5, and large = 0.8, and for Cramer’s V: small = 0.10, medium = 0.30, and large = 0.50.

Risk of bias assessment

JS and CWS independently evaluated the risk of bias for all studies using two distinct assessment tools. We used the Cochrane risk of bias tool RoB 2 [ 28 ] to assess the risk of bias in the RCTs. With the RoB 2 tool, the bias was assessed as high, some concerns or low for five domains: randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result [ 28 ].

We used the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool [ 29 ] to assess the risk of bias in the cohort and single-group studies. By using ROBINS-I for the non-randomized trials, the risk of bias was assessed using the categories low, moderate, serious, critical or no information for seven domains: confounding, selection of participants, classification of interventions, deviations from intended interventions, missing data, measurement of outcomes, and selection of the reported result [ 29 ].

We included eight studies in the review (Fig.  1 ). An overview of the included studies is presented in detail in Table  1 .

Four studies were RCTs [ 19 , 20 , 21 , 22 ], two were single group pretest–posttest studies [ 23 , 26 ], one was a controlled before and after study [ 25 ], and one was a cohort study [ 24 ]. The studies included health professionals from diverse educational backgrounds, including some from mental health and medical services, as well as students in medicine, social work, and nursing. All studies, published from 2009 to 2021, utilized non-immersive VR desktop system interventions featuring various forms of VP designs. Based on an updated classification of VP interventions by Kononowicz et al. [ 30 ] developed from a model proposed by Talbot et al. [ 31 ], we have described the characteristics of the interventions in Table  1 . Four of the studies utilized a virtual standardized patient (VSP) intervention [ 20 , 21 , 22 , 23 ], a conversational agent that simulates clinical presentations for training purposes. Two studies employed an interactive patient scenario (IPS) design [ 25 , 26 ], an approach that primarily uses text-based multimedia, enhanced with images and case histories through text or voice narratives, to simulate clinical scenarios. Lastly, two studies used a virtual patient game (VP game) intervention [ 19 , 24 ]. These interventions feature training scenarios using 3D avatars, specifically designed to improve clinical reasoning and team training skills. It should be noted that the interventions classified as VSPs in this review, being a few years old, do not encompass artificial intelligence (AI) as we interpret it today. However, since the interventions include some kind of algorithm that provides answers to questions, we consider them as conversational agents, and therefore as VSPs. As the eight included studies varied significantly in terms of design, interventions, and outcome measures, we could not incorporate them into a meta-analysis.

The overall risk of bias for the four RCTs was high [ 19 , 20 , 22 ] or of some concern [ 21 ] (Fig.  2 ). They were all assessed as low or of some concern in the domains of randomization. Three studies were assessed with a high risk of bias in one [ 19 , 20 ] or two domains [ 22 ]; one study had a high risk of bias in the domain of selection of the reported result [ 19 ], one in the domain of measurement of outcome [ 20 ], and one in the domains of deviation from the intended interventions and missing outcome data [ 22 ]. One study was not assessed as having a high risk of bias in any domain [ 21 ].

figure 2

Risk of bias summary: review authors assessments of each risk of bias item in the included RCT studies

For the four non-randomized studies, the overall risk of bias was judged to be moderate [ 26 ] or serious [ 23 , 24 , 25 ] (Fig.  3 ). One study had a serious risk of bias in two domains: confounding and measurement of outcomes [ 23 ]. Two studies had a serious risk of bias in one domain, namely confounding [ 24 , 25 ], while one study was judged not to have a serious risk of bias in any domain [ 26 ].

figure 3

Risk of bias summary: review authors assessments of each risk of bias item in the included non-randomized studies

Three studies investigated the impact of virtual reality training on mental health knowledge [ 24 , 25 , 26 ]. One study with 32 resident psychiatrists in a single group pretest–posttest design assessed the effect of a VR training intervention on knowledge of posttraumatic stress disorder (PTSD) symptomatology, clinical management, and communication skills [ 26 ]. The intervention consisted of an IPS. The assessment of the outcome was conducted using a knowledge test with 11 multiple-choice questions and was administered before and after the intervention. This study reported a significant improvement on the knowledge test after the VR training intervention.

The second study examined the effect of a VR training intervention on knowledge of dementia [ 25 ], employing a controlled before and after design. Seventy-nine medical students in clinical training were divided into two groups, following a traditional learning program. The experimental group received an IPS intervention. The outcome was evaluated with a knowledge test administered before and after the intervention with significantly higher posttest scores in the experimental group than in the control group, with a moderate effects size observed between the groups.

A third study evaluated the effect of a VR training intervention on 299 undergraduate nursing students’ diagnostic recognition of depression and schizophrenia (classified as knowledge) [ 24 ]. In a prospective cohort design, the VR intervention was the only difference in the mental health related educational content provided to the two cohorts, and consisted of a VP game design, developed to simulate training situations with virtual patient case scenarios, including depression and schizophrenia. The outcome was assessed by determining the accuracy of diagnoses made after reviewing case vignettes of depression and schizophrenia. The study found no statistically significant effect of VR training on diagnostic accuracy between the simulation and the non-simulation cohort.

Summary: All three studies assessing the effect of a VR intervention on knowledge were non-randomized studies with different study designs using different outcome measures. Two studies used an IPS design, while one study used a VP game design. Two of the studies found a significant effect of VR training on knowledge. Of these, one study had a moderate overall risk of bias [ 26 ], while the other was assessed as having a serious overall risk of bias [ 25 ]. The third study, which did not find any effect of the virtual reality intervention on knowledge, was assessed to have a serious risk of bias [ 24 ].

Three RCTs assessed the effectiveness of VR training on skills [ 20 , 21 , 22 ]. One of them evaluated the effect of VR training on clinical skills in alcohol screening and intervention [ 20 ]. In this study, 102 health care professionals were randomly allocated to either a group receiving no training or a group receiving a VSP intervention. To evaluate the outcome, three standardized patients rated each participant using a checklist based on clinical criteria. The VSP intervention group demonstrated significantly improved posttest skills in alcohol screening and brief intervention compared to the control group, with moderate and small effect sizes, respectively.

Another RCT, including 67 medical college students, evaluated the effect of VR training on clinical skills by comparing the frequency of questions asked about suicide in a VSP intervention group and a video module group [ 21 ]. The assessment of the outcome was a psychiatric interview with a standardized patient. The primary outcome was the frequency with which the students asked the standardized patient five questions about suicide risk. Minimal to small effect sizes were noted in favor of the VSP intervention, though they did not achieve statistical significance for any outcomes.

One posttest only RCT evaluated the effect of three training programs on skills in detecting and diagnosing major depressive disorder and posttraumatic stress disorder (PTSD) [ 22 ]. The study included 30 family physicians, and featured interventions that consisted of two different VSPs designed to simulate training situations, and one text-based program. A diagnostic form filled in by the participants after the intervention was used to assess the outcome. The results revealed a significant effect on diagnostic accuracy for major depressive disorder for both groups receiving VR training, compared to the text-based program, with large effect sizes observed. For PTSD, the intervention using a fixed avatar significantly improved diagnostic accuracy with a large effect size, whereas the intervention with a choice avatar demonstrated a moderate to large effect size compared to the text-based program.

Summary: Three RCTs assessed the effectiveness of VR training on clinical skills [ 20 , 21 , 22 ], all of which used a VSP design. To evaluate the effect of training, two of the studies utilized standardized patients with checklists. The third study measured the effect on skills using a diagnostic form completed by the participants. Two of the studies found a significant effect on skills [ 20 , 22 ], both were assessed to have a high risk of bias. The third study, which did not find any effect of VR training on skills, had some concern for risk of bias [ 21 ].

Knowledge and skills

One RCT study with 227 health care professionals assessed knowledge and skills as a combined outcome compared to a waitlist control group, using a self-report survey before and after the VR training [ 19 ]. The training intervention was a VP game designed to practice knowledge and skills related to mental health and substance abuse disorders. To assess effect of the training, participants completed a self-report scale measuring perceived knowledge and skills. Changes between presimulation and postsimulation scores were reported only for the within treatment group ( n  = 117), where the composite postsimulation score was significantly higher than the presimulation score, with a large effect size observed. The study was judged to have a high risk of bias in the domain of selection of the reported result.

One single group pretest–posttest study with 100 social work and nursing students assessed the effect of VSP training on attitudes towards individuals with substance abuse disorders [ 23 ]. To assess the effect of the training, participants completed an online pretest and posttest survey including questions from a substance abuse attitudes survey. This study found no significant effect of VR training on attitudes and was assessed as having a serious risk of bias.

Perceived competence

The same single group pretest–posttest study also assessed the effect of a VSP training intervention on perceived competence in screening, brief intervention, and referral to treatment in encounters with patients with substance abuse disorders [ 23 ]. A commonly accepted definition of competence is that it comprises integrated components of knowledge, skills, and attitudes that enable the successful execution of a professional task [ 32 ]. To assess the effect of the training, participants completed an online pretest and posttest survey including questions on perceived competence. The study findings demonstrated a significant increase in perceived competence following the VSP intervention. The risk of bias in this study was judged as serious.

This systematic review aimed to investigate the effectiveness of VR training on knowledge, skills, and attitudes that health professionals need to master in the assessment and treatment of mental health disorders. A narrative synthesis of eight included studies identified VR training interventions that varied in design and educational content. Although mixed results emerged, most studies reported improvements in knowledge and skills after VR training.

We found that all interventions utilized some type of VP design, predominantly VSP interventions. Although our review includes a limited number of studies, it is noteworthy that the distribution of interventions contrasts with a literature review on the use of ‘virtual patient’ in health care education from 2015 [ 30 ], which identified IPS as the most frequent intervention. This variation may stem from our review’s focus on the mental health field, suggesting a different intervention need and distribution than that observed in general medical education. A fundamental aspect of mental health education involves training skills needed for interpersonal communication, clinical interviews, and symptom assessment, which makes VSPs particularly appropriate. While VP games may be suitable for clinical reasoning in medical fields, offering the opportunity to perform technical medical procedures in a virtual environment, these designs may present some limitations for skills training in mental health education. Notably, avatars in a VP game do not comprehend natural language and are incapable of engaging in conversations. Therefore, the continued advancement of conversational agents like VSPs is particularly compelling and considered by scholars to hold the greatest potential for clinical skills training in mental health education [ 3 ]. VSPs, equipped with AI dialogue capabilities, are particularly valuable for repetitive practice in key skills such as interviewing and counseling [ 31 ], which are crucial in the assessment and treatment of mental health disorders. VSPs could also be a valuable tool for the implementation of training methods in mental health education, such as deliberate practice, a method that has gained attention in psychotherapy training in recent years [ 33 ] for its effectiveness in refining specific performance areas through consistent repetition [ 34 ]. Within this evolving landscape, AI system-based large language models (LLMs) like ChatGPT stand out as a promising innovation. Developed from extensive datasets that include billions of words from a variety of sources, these models possess the ability to generate and understand text in a manner akin to human interaction [ 35 ]. The integration of LLMs into educational contexts shows promise, yet careful consideration and thorough evaluation of their limitations are essential [ 36 ]. One concern regarding LLMs is the possibility of generating inaccurate information, which represents a challenge in healthcare education where precision is crucial [ 37 ]. Furthermore, the use of generative AI raises ethical questions, notably because of potential biases in the training datasets, including content from books and the internet that may not have been verified, thereby risking the perpetuation of these biases [ 38 ]. Developing strategies to mitigate these challenges is imperative, ensuring LLMs are utilized safely in healthcare education.

All interventions in our review were based on non-immersive desktop VR systems, which is somewhat surprising considering the growing body of literature highlighting the impact of immersive VR technology in education, as exemplified by reviews such as that of Radianti et al. [ 39 ]. Furthermore, given the recent accessibility of affordable, high-quality head-mounted displays, this observation is noteworthy. Research has indicated that immersive learning based on head-mounted displays generally yields better learning outcomes than non-immersive approaches [ 40 ], making it an interesting research area in mental health care training and education. Studies using immersive interventions were excluded in the present review because of methodological concerns, paralleling findings described in a systematic review on immersive VR in education [ 41 ], suggesting the potential early stage of research within this field. Moreover, the integration of immersive VR technology into mental health care education may encounter challenges associated with complex ethical and regulatory frameworks, including data privacy concerns exemplified by the Oculus VR headset-Facebook integration, which could restrict the implementation of this technology in healthcare setting. Prioritizing specific training methodologies for enhancing skills may also affect the utilization of immersive VR in mental health education. For example, integrating interactive VSPs into a fully immersive VR environment remains a costly endeavor, potentially limiting the widespread adoption of immersive VR in mental health care. Meanwhile, the use of 360-degree videos in immersive VR environments for training purposes [ 42 ] can be realized with a significantly lower budget. Immersive VR offers promising opportunities for innovative training, but realizing its full potential in mental health care education requires broader research validation and the resolution of existing obstacles.

This review bears some resemblance to the systematic review by Jensen et al. on virtual patients in undergraduate psychiatry education [ 13 ] from 2024, which found that virtual patients improved learning outcomes compared to traditional methods. However, these authors’ expansion of the commonly used definition of virtual patient makes their results difficult to compare with the findings in the present review. A recognized challenge in understanding VR application in health care training arises from the literature on VR training for health care personnel, where ‘virtual patient’ is a term broadly used to describe a diverse range of VR interventions, which vary significantly in technology and educational design [ 3 , 30 ]. For instance, reviews might group different interventions using various VR systems and designs under a single label (virtual patient), or primary studies may use misleading or inadequately defined classifications for the virtual patient interventions evaluated. Clarifying the similarities and differences among these interventions is vital to inform development and enhance communication and understanding in educational contexts [ 43 ].

Strengths and limitations

To the best of our knowledge, this is the first systematic review to evaluate the effectiveness of VR training on knowledge, skills, and attitudes in health care professionals and students in assessing and treating mental health disorders. This review therefore provides valuable insights into the use of VR technology in training and education for mental health care. Another strength of this review is the comprehensive search strategy developed by a senior academic librarian at Inland Norway University of Applied Sciences (HINN) and the authors in collaboration with an adviser from KildeGruppen AS (a Norwegian media company). The search strategy was peer-reviewed by an academic librarian at HINN. Advisers from VRINN (an immersive learning cluster in Norway) and SIMInnlandet (a center for simulation in mental health care at Innlandet Hospital Trust) provided assistance in reviewing the VR systems of the studies, while the classification of the learning designs was conducted under the guidance of a VP scholar. This systematic review relies on an established and recognized classification of VR interventions for training health care personnel and may enhance understanding of the effectiveness of VR interventions designed for the training of mental health care personnel.

This review has some limitations. As we aimed to measure the effect of the VR intervention alone and not the effect of a blended training design, the selection of included studies was limited. Studies not covered in this review might have offered different insights. Given the understanding that blended learning designs, where technology is combined with other forms of learning, have significant positive effects on learning outcomes [ 44 ], we were unable to evaluate interventions that may be more effective in clinical settings. Further, by limiting the outcomes to knowledge, skills, and attitudes, we might have missed insights into other outcomes that are pivotal to competence acquisition.

Limitations in many of the included studies necessitate cautious interpretation of the review’s findings. Small sample sizes and weak designs in several studies, coupled with the use of non-validated outcome measures in some studies, diminish the robustness of the findings. Furthermore, the risk of bias assessment in this review indicates a predominantly high or serious risk of bias across most of the studies, regardless of their design. In addition, the heterogeneity of the studies in terms of study design, interventions, and outcome measures prevented us from conducting a meta-analysis.

Further research

Future research on the effectiveness of VR training for specific learning outcomes in assessing and treating mental health disorders should encompass more rigorous experimental studies with larger sample sizes. These studies should include verifiable descriptions of the VR interventions and employ validated tools to measure outcomes. Moreover, considering that much professional learning involves interactive and reflective practice, research on VR training would probably be enhanced by developing more in-depth study designs that evaluate not only the immediate learning outcomes of VR training but also the broader learning processes associated with it. Future research should also concentrate on utilizing immersive VR training applications, while additionally exploring the integration of large language models to augment interactive learning in mental health care. Finally, this review underscores the necessity in health education research involving VR to communicate research findings using agreed terms and classifications, with the aim of providing a clearer and more comprehensive understanding of the research.

This systematic review investigated the effect of VR training interventions on knowledge, skills, and attitudes in the assessment and treatment of mental health disorders. The results suggest that VR training interventions can promote knowledge and skills acquisition. Further studies are needed to evaluate VR training interventions as a learning tool for mental health care providers. This review emphasizes the necessity to improve future study designs. Additionally, intervention studies of immersive VR applications are lacking in current research and should be a future area of focus.

Availability of data and materials

Detailed search strategies from each database is available in the DataverseNO repository, https://doi.org/10.18710/TI1E0O .

Abbreviations

Virtual Reality

Cave Automatic Virtual Environment

Randomized Controlled Trial

Non-Randomized study

Virtual Standardized Patient

Interactive Patient Scenario

Virtual Patient

Post Traumatic Stress Disorder

Standardized Patient

Artificial intelligence

Inland Norway University of Applied Sciences

Doctor of Philosophy

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Acknowledgements

The authors thank Mole Meyer, adviser at SIMInnlandet, Innlandet Hospital Trust, and Keith Mellingen, manager at VRINN, for their assistance with the categorization and classification of VR interventions, and Associate Professor Inga Hege at the Paracelcus Medical University in Salzburg for valuable contributions to the final classification of the interventions. The authors would also like to thank Håvard Røste from the media company KildeGruppen AS, for assistance with the search strategy; Academic Librarian Elin Opheim at the Inland Norway University of Applied Sciences for valuable peer review of the search strategy; and the Library at the Inland Norway University of Applied Sciences for their support. Additionally, we acknowledge the assistance provided by OpenAI’s ChatGPT for support with translations and language refinement.

Open access funding provided by Inland Norway University Of Applied Sciences The study forms a part of a collaborative PhD project funded by South-Eastern Norway Regional Health Authority through Innlandet Hospital Trust and the Inland University of Applied Sciences.

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Cathrine W. Steen & Kerstin Söderström

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Cathrine W. Steen, Kerstin Söderström & Inger Beate Nylund

Norwegian National Advisory Unit On Concurrent Substance Abuse and Mental Health Disorders, Innlandet Hospital Trust, P.B 104, Brumunddal, 2381, Norway

Bjørn Stensrud

Akershus University Hospital, P.B 1000, Lørenskog, 1478, Norway

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National Centre for Suicide Research and Prevention, Oslo, 0372, Norway

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Contributions

CWS, KS, BS, and JS collaboratively designed the study. CWS and JS collected and analysed the data and were primarily responsible for writing the manuscript text. All authors contributed to the development of the search strategy. IBN conducted the literature searches and authored the chapter on the search strategy in the manuscript. All authors reviewed, gave feedback, and granted their final approval of the manuscript.

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Correspondence to Cathrine W. Steen .

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Supplementary Information

Additional file 1: table 2..

Effects of VR training in the included studies: Randomized controlled trials (RCTs) and non-randomized studies (NRSs).

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Steen, C.W., Söderström, K., Stensrud, B. et al. The effectiveness of virtual reality training on knowledge, skills and attitudes of health care professionals and students in assessing and treating mental health disorders: a systematic review. BMC Med Educ 24 , 480 (2024). https://doi.org/10.1186/s12909-024-05423-0

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Received : 19 January 2024

Accepted : 12 April 2024

Published : 01 May 2024

DOI : https://doi.org/10.1186/s12909-024-05423-0

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  • Health care professionals
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  • Virtual reality
  • Mental health
  • Clinical skills
  • Systematic review

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mental health literature review example

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