Characterizing modifications to the mental health gap action programme (mhGAP) intervention guide during implementation in low- and middle-income countries using the framework for reporting adaptations and modifications to evidence-based interventions: a systematic review of reviews
Bibliographic record
Abstract
BACKGROUND: Low- and middle-income countries (LMICs) allocate a disproportionately small fraction of their healthcare budgets to mental health, leading to a treatment gap exceeding 75%. To address this disparity, the World Health Organization (WHO) introduced the Mental Health Gap Action Programme (mhGAP), aiming to integrate mental healthcare into primary and community care settings. Central to this initiative is task-sharing: empowering non-specialist healthcare providers to detect and treat mental disorders. Adaptation and modification of mhGAP to the national and local contexts is an integral aspect of the guidelines. METHODS: This systematic review of reviews employs the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) to document and characterize modifications to mhGAP implementation in LMICs. The databases searched included Embase, PubMed, PsycINFO, CINAHL, Google Scholar, Cochrane, and Web of Science. Reviews selected in stage 1 were used to find empirical studies from which relevant data was extracted. RESULTS: Narrative synthesis suggests that modifications primarily focus on content, delivery, and training methods, with limited attention to scaling up. Modifications adopt top down, yet consultative and participatory approaches. There is a notable lack of reporting on challenges, processes, and outcomes. Recommendations have been made to expand FRAME, namely, sources of knowledge, financial and temporal resources employed during the process of modification. CONCLUSION: Modifications are essential for adapting interventions to diverse settings, yet they are often researcher-led with limited stakeholder involvement. Better documentation-particularly on challenges and outcomes-is needed. Strengthening frameworks like FRAME can improve reporting, optimize resources, and enhance implementation and scale-up in similar contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".