Review of layperson screening tools and model for a holistic mental health screener in lower and middle income countries
Bibliographic record
Abstract
Abstract Background The needs of people diagnosed with Mental Neurological and Substance-Use (MNS) conditions are complex including interactions physical, social, medical and environmental factors. Treatment requires a multidisciplinary approach including health and social services at different levels of care. However, due to inadequate assessment, services and scarcity of human resource for mental health, treatment of persons diagnosed with MNS conditions in many LMICs is mainly facility-based pharmacotherapy with minimal non-pharmacology treatments and social support services. In low resource settings, gaps in human resource capacity may be met using layperson health workers. A layperson health working is one without formal mental health training and may be equivalent to community health worker (CHW) or less cadre in primary health care system. Objectives This study reviewed layperson mental health screening tools for use in supporting mental health in developing countries, including the content and psychometric properties of the tools. Based on this review this study proposes recommendations for the design and effective use of layperson mental health screening tools based on the Five Pillars of global mental health. Methods A systematic review was used to identify and examine the use of mental health screening tools among laypersons supporting community-based mental health programs. PubMed, Scopus, CINAHL and PsychInfo databases were reviewed using a comprehensive list of keywords and MESH terms that included mental health, screening tools, lay-person, lower and middle income countries. Articles were included if they describe mental health screening tools used by laypersons for screening, delivery or monitoring of MNS conditions in community-based program in LMICs. Diagnostic tools were not included in this study. Trained research interviewers or research assistants were not considered as lay health workers for this study. Results There were eleven studies retained after 633 were screened. Twelve tools were identified covering specific disorders (E.g. alcohol and substance use, subcortical dementia associated with HIV/AIDS, PTSD) or common mental disorders (mainly depression and anxiety). These tools have been tested in LMICs including South Africa, Zimbabwe, Haiti, Malaysia, Pakistan, India, Ethiopia and Brazil. The included studies show that simple screening tools can enhance the value of laypersons and better support their roles in providing community-based mental health support. However, most of the layperson MH screening tools used in LMICs do not provide comprehensive information that can inform integrated comprehensive treatment planning and understanding of the broader mental health needs of the community. Conclusion Developing a layperson screening tools is vital for integrated community-based mental health intervention. This study proposed a holistic framework which considers the relationship between individual’s physical, mental and spiritual aspect of mental health, interpersonal as well as broader contextual determinants (community, policy and different level of the health system) that can be consulted for developing or selecting a layperson mental health screening instrument. More research are needed to evaluate the practical application of this framework.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".