Psychological Treatments for the World: Lessons from Low- and Middle-Income Countries
Bibliographic record
Abstract
Common mental disorders, including depression, anxiety, and posttraumatic stress, are leading causes of disability worldwide. Treatment for these disorders is limited in low- and middle-income countries. This systematic review synthesizes the implementation processes and examines the effectiveness of psychological treatments for common mental disorders in adults delivered by nonspecialist providers in low- and middle-income countries. In total, 27 trials met the eligibility criteria; most treatments targeted depression or posttraumatic stress. Treatments were commonly delivered by community health workers or peers in primary care or community settings; they usually were delivered with fewer than 10 sessions over 2-3 months in an individual, face-to-face format. Treatments included common elements, such as nonspecific engagement and specific domains of behavioral, interpersonal, emotional, and cognitive elements. The pooled effect size was 0.49 (95% confidence interval = 0.36-0.62), favoring intervention conditions. Our review demonstrates that psychological treatments-comprising a parsimonious set of common elements and delivered by a low-cost, widely available human resource-have moderate to strong effects in reducing the burden of common mental disorders.
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 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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".