A scoping review of the literature on the application and usefulness of the Problem Management Plus (PM+) intervention around the world
Bibliographic record
Abstract
BACKGROUND: Given the high rates of common mental disorders and limited resources, task-shifting psychosocial interventions are needed to provide adequate care. One such intervention developed by the World Health Organization is Problem Management Plus (PM+). AIMS: This review maps the evidence regarding the extent of application and usefulness of the PM+ intervention, i.e. adaptability, feasibility, effectiveness and scalability, since it was introduced in 2016. METHOD: We conducted a scoping review of seven literature databases and grey literature from January 2015 to February 2024, to identify peer-reviewed and grey literature on PM+ around the world. RESULTS: Out of 6739 potential records, 42 met the inclusion criteria. About 60% of the included studies were from low- and middle-income countries. Findings from pilot/feasibility trials demonstrated that PM+ is feasible, acceptable and safe. Results from definitive randomised controlled trials at short-term follow-up also suggested that PM+ is effective, with overall moderate-to-large effect sizes, in improving symptoms of common mental health problems. Although PM+ was more effective in reducing symptoms of common mental disorders, it was found to be costlier compared to usual care in the only study that evaluated its cost-effectiveness. CONCLUSIONS: Our findings indicate that PM+, in its individual and group formats, can be adapted and effectively delivered by trained helpers to target a wide range of common mental health concerns. More effectiveness and implementation evidence is required to understand the long-term impact of PM+, its cost-effectiveness and scalability, and moderators of treatment outcomes such as gender and delivery formats.
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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 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".