WHO mental health gap action programme (mhGAP) intervention guide: updated systematic review on evidence and impact
Bibliographic record
Abstract
QUESTION: There is a large worldwide gap between the service need and provision for mental, neurological and substance use disorders. WHO's Mental Health Gap Action Programme (mhGAP) intervention guide (IG), provides evidence-based guidance and tools for assessment and integrated management of priority disorders. Our 2017 systematic review identified 33 peer-reviewed studies describing mhGAP-IG implementation in low-income and middle-income countries. STUDY SELECTION AND ANALYSIS: We searched MEDLINE, Embase, PsycINFO, Web of Knowledge, Scopus, CINAHL, LILACS, ScieELO, Cochrane, PubMed databases, 3ie, Google Scholar and citations of our review, on 24 November 2020. We sought evidence, experience and evaluations of the mhGAP-IG, app or mhGAP Humanitarian IG, from any country, in any language. We extracted data from included papers, but heterogeneity prevented meta-analysis. FINDINGS: Of 2621 results, 162 new papers reported applications of the mhGAP-IG. They described mhGAP training courses (59 references), clinical applications (n=49), research uses (n=27), contextual adaptations (n=13), economic studies (n=7) and other educational applications (n=7). Most were conducted in the African region (40%) and South-East Asia (25%). Studies demonstrated improved knowledge, attitudes and confidence post-training and improved symptoms and engagement with care, post-implementation. Research studies compared mhGAP-IG-enhanced usual care with task-shared psychological interventions and adaptation studies optimised mhGAP-IG implementation for different contexts. Economic studies calculated human resource requirements of scaling up mhGAP-IG implementation and other educational studies explored its potential for repurposing. CONCLUSIONS: The diverse, expanding global mhGAP-IG literature demonstrates substantial impact on training, patient care, research and practice. Priorities for future research should be less-studied regions, severe mental illness and contextual adaptation of brief psychological interventions.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".