Update of the World Health Organization’s Mental Health Gap Action Programme Guideline for Psychoses (Including Schizophrenia)
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: The World Health Organization's (WHOs) Mental Health Gap Action Programme (mhGAP) aims to improve healthcare for mental, neurological, and substance use disorders in nonspecialized settings, with a focus on low- and middle-income countries (LMICs). mhGAP includes guidelines for the treatment of psychoses (including schizophrenia), which were recently updated in 2023. The complexity of the WHO guideline update process and the updated recommendations on psychoses are presented. STUDY DESIGN: The WHO guideline development process is outlined as well as the evidence appraisal and the translation of the evidence into recommendations following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. The guideline update process includes a review of the literature, a compilation of systematic reviews, and extracting data related to critical and important outcomes. The updated recommendations and the justifying evidence are discussed. STUDY RESULTS: The WHO mhGAP guidelines for psychoses are adapted to LMICs, and consist of 13 recommendations in 2023, whereof 5 were updated, and 1 recommendation was newly developed. Background information on how these recommendations were obtained, and significant changes since the previous guideline update in 2015 are provided. CONCLUSIONS: Unlike other guidelines, the WHO must consider various countries, contextual factors, and the WHO Model Lists of Essential Medicines when developing its guidelines. A transformation of the WHO guideline for psychoses into a living guideline would ensure always up-to-date recommendations and facilitate shared decision-making.
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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 | 0.034 | 0.102 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".