Mental Health Screening Approaches for Resettling Refugees and Asylum Seekers: A Scoping Review
Bibliographic record
Abstract
Refugees and asylum seekers often face delayed mental health diagnoses, treatment, and care. COVID-19 has exacerbated these issues. Delays in diagnosis and care can reduce the impact of resettlement services and may lead to poor long-term outcomes. This scoping review aims to characterize studies that report on mental health screening for resettling refugees and asylum seekers pre-departure and post-arrival to a resettlement state. We systematically searched six bibliographic databases for articles published between 1995 and 2020 and conducted a grey literature search. We included publications that evaluated early mental health screening approaches for refugees of all ages. Our search identified 25,862 citations and 70 met the full eligibility criteria. We included 45 publications that described mental health screening programs, 25 screening tool validation studies, and we characterized 85 mental health screening tools. Two grey literature reports described pre-departure mental health screening. Among the included publications, three reported on two programs for women, 11 reported on programs for children and adolescents, and four reported on approaches for survivors of torture. Programs most frequently screened for overall mental health, PTSD, and depression. Important considerations that emerged from the literature include cultural and psychological safety to prevent re-traumatization and digital tools to offer more private and accessible self-assessments.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".