Pilot study of the prevalence of alcohol, substance use and mental disorders in a cohort of <scp>I</scp>raqi, <scp>A</scp>fghani, and <scp>I</scp>ranian refugees in <scp>V</scp>ancouver
Bibliographic record
Abstract
Abstract Introduction This study investigated the prevalence rate of current alcohol, substance use, post‐traumatic stress disorder (PTSD) and depression in a cohort of Middle‐Eastern government‐assisted refugees (GAR) to British Columbia. Methods A group of GARs (32 men and 36 women) were interviewed and assessed using four clinical assessments (AUDIT, DUDIT, IES‐R, PHQ‐9) at an intake facility in Metro Vancouver. Results Men had a higher prevalence of alcohol use compared to women (15.6% versus 0%, P < .05). Substance use was low for both men and women (3.1% and 2.8%) and consisted of Tylenol 3 use. Approximately the same proportion of male and female GARs had PTSD as a clinical concern (21.8% and 22.2%). GARs that were Iraqi, Muslim, had attended university, or had children had higher IES‐R scores compared to other GARs. Depression as a clinical concern was found in female and male GARs (16.7% and 21.9%). Total PHQ‐9 scores were higher for GARs who were unmarried. Discussion It would be beneficial for mental health care providers who treat Middle‐Eastern GARs to review screening methods for PTSD, depression, and substance use. This may also include additional training in the screening of GARs for appropriate mental health services. Mental health services must be linguistically and culturally matched to the recipient for optimal therapeutic benefit.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".