Depression-level symptoms among Syrian refugees: findings from a Canadian longitudinal study
Bibliographic record
Abstract
Background Canada launched the Syrian Refugee Resettlement Initiative in 2015 and resettled over 40,000 refugees.Aim To evaluate the prevalence of depression-level symptoms at baseline and one year post-resettlement and analyze its predictors.Methods Data come from the Syrian Refugee Integration and Long-term Health Outcomes in Canada study (SyRIA.lth) involving 1924 Syrian refugees recruited through a variety of community-based strategies. Data were collected using structured interviews in 2017 and 2018. Depression symptoms were measured using Patient Health Questionnaire 9 (PHQ-9). Analysis for associated factors was executed using multinomial logistic regression.Results Mean age was 38.5 years (SD 13.8). Sample included 49% males and 51% females settled in Ontario (48%), Quebec (36%) and British Columbia (16%). Over 74% always needed an interpreter, and only 23% were in employment. Prevalence of depression-level symptoms was 15% at baseline and 18% in year-2 (p < 0.001). Significant predictors of depression-level symptoms at year-2 were baseline depression, sponsorship program, province, poor language skills, lack of satisfaction with housing conditions and with health services, lower perceived control, lower perceived social support and longer stay in Canada.Conclusion Increase in depression-level symptoms deserves attention through focusing on identified predictors particularly baseline depression scores, social support, perceived control and language ability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".