The Role of Communities in Mental Health Care in Low- and Middle-Income Countries: A Meta-Review of Components and Competencies
Bibliographic record
Abstract
, and the Action Plan of the World Psychiatric Association. There is increasing evidence for effectiveness of mental health interventions delivered by non-specialists in community platforms in low- and middle-income countries (LMIC). However, the role of community components has yet to be summarized. Our objective was to map community interventions in LMIC, identify competencies for community-based providers, and highlight research gaps. Using a review-of-reviews strategy, we identified 23 reviews for the narrative synthesis. Motivations to employ community components included greater accessibility and acceptability compared to healthcare facilities, greater clinical effectiveness through ongoing contact and use of trusted local providers, family involvement, and economic benefits. Locations included homes, schools, and refugee camps, as well as technology-aided delivery. Activities included awareness raising, psychoeducation, skills training, rehabilitation, and psychological treatments. There was substantial variation in the degree to which community components were integrated with primary care services. Addressing gaps in current practice will require assuring collaboration with service users, utilizing implementation science methods, creating tools to facilitate community services and evaluate competencies of providers, and developing standardized reporting for community-based programs.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".