Traumatic Experiences and Mental Health Risk for Refugees
Bibliographic record
Abstract
Refugees who settle in Western countries exhibit a high rate of mental health issues, which are often related to experiences throughout the pre-displacement, displacement, and post-displacement processes. Early detection of mental health symptoms could increase positive outcomes in this vulnerable population. The rates and predictors of positive screenings for mental health symptoms were examined among a large sample of refugees, individuals with special immigrant visas, and parolees/entrants (N = 8149) from diverse nationalities. Logistic regression analyses were used to determine if demographic factors and witnessing/experiencing violence predicted positive screenings. On a smaller subset of the sample, we calculated referral acceptance rate by country of origin. Refugees from Syria, Iraq, and Afghanistan were most likely to exhibit a positive screening for mental health symptoms. Refugees from Sudan, Iraq, and Syria reported the highest rate of experiencing violence, whereas those from Iraq, Sudan, and the Democratic Republic of Congo reported the highest rate of witnessing violence. Both witnessing and experiencing violence predicted positive Refugee Health Screener-15 (RHS-15) scores. Further, higher age and female gender predicted positive RHS-15 scores, though neither demographic variable was correlated with accepting a referral for mental health services. The findings from this study can help to identify characteristics that may be associated with risk for mental health symptoms among a refugee population.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".