Understanding Supporting and Hindering Factors in Community-Based Psychotherapy for Refugees: A Realist-Informed Systematic Review
Bibliographic record
Abstract
Culture, tradition, structural violence, and mental health-related stigma play a major role in global mental health for refugees. Our aim was to understand what factors determine the success or failure of community-based psychotherapy for trauma-affected refugees and discuss implications for primary health care programs. Using a systematic realist-informed approach, we searched five databases from 2000 to 2018. Two reviewers independently selected RCTs for inclusion, and we contacted authors to obtain therapy training manuals. Fifteen articles and 11 training manuals met our inclusion criteria. Factors that improved symptoms of depression, anxiety, and PTSD included providing culturally adapted care in a migrant-sensitive setting, giving a role to other clinical staff (task-shifting), and intervention intensity. Precarious asylum status, constraining program monitoring requirements, and diverse socio-cultural and gender needs within a setting may reduce the effectiveness of the program. Primary care programs may enable community based mental health care and may reduce mental health-related stigma for refugees and other migrants. More research is needed on the cultural constructs of distress, programs delivered in primary care, and the role of cultural and language interpretation services in mental health care.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".