The Social Determinants of Refugee Mental Health in the Post-Migration Context: A Critical Review
Bibliographic record
Abstract
With the global increase in the number of refugees and asylum seekers, mental health professionals have become more aware of the need to understand and respond to the mental health needs of forced migrants. This critical review summarizes the findings of recent systematic reviews and primary research on the impact of post-migration conditions on mental disorders and PTSD among refugees and asylum seekers. Historically, the focus of mental health research and interventions with these populations has been on the impact of pre-migration trauma. Pre-migration trauma does predict mental disorders and PTSD, but the post-migration context can be an equally powerful determinant of mental health. Moreover, post-migration factors may moderate the ability of refugees to recover from pre-migration trauma. The importance of post-migration stressors to refugee mental health suggests the need for therapeutic interventions with psychosocial elements that address the broader conditions of refugee and asylum seekers' lives. However, there are few studies of multimodal interventions with refugees, and even fewer with control conditions that allow for conclusions about their effectiveness. These findings are interpreted using a social determinants of health framework that connects the risk and protective factors in the material and social conditions of refugees' post-migration lives to broader social, economic and political factors.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".