Afghan mental health and psychosocial well-being: thematic review of four decades of research and interventions
Bibliographic record
Abstract
BACKGROUND: Four decades of war, political upheaval, economic deprivation and forced displacement have profoundly affected both in-country and refugee Afghan populations. AIMS: We reviewed literature on mental health and psychosocial well-being, to assess the current evidence and describe mental healthcare systems, including government programmes and community-based interventions. METHOD: = 214 papers). We identified the main factors driving the epidemiology of mental health problems, culturally salient understandings of psychological distress, coping strategies and help-seeking behaviours, and interventions for mental health and psychosocial support. RESULTS: Mental health problems and psychological distress show higher risks for women, ethnic minorities, people with disabilities and youth. Issues of suicidality and drug use are emerging problems that are understudied. Afghans use specific vocabulary to convey psychological distress, drawing on culturally relevant concepts of body-mind relationships. Coping strategies are largely embedded in one's faith and family. Over the past two decades, concerted efforts were made to integrate mental health into the nation's healthcare system, train cadres of psychosocial counsellors, and develop community-based psychosocial initiatives with the help of non-governmental organisations. A small but growing body of research is emerging around psychological interventions adapted to Afghan contexts and culture. CONCLUSIONS: We make four recommendations to promote health equity and sustainable systems of care. Interventions must build cultural relevance, invest in community-based psychosocial support and evidence-based psychological interventions, maintain core mental health services at logical points of access and foster integrated systems of 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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".