User and Provider Perspectives on Improving Mental Healthcare for Syrian Refugee Women in Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
Introduction: Refugees have higher risk of developing mental illness like anxiety, depression and Post-Traumatic Stress Disorder as they flee from violence. Women refugees may have unique mental healthcare needs due to their vulnerability to gender-based violence and abuse during flight from war. The research question of this study was what the health system can do better to address the mental healthcare needs of refugee women in Winnipeg. Methods: Semi-structured interviews were conducted with 9 Syrian refugee women and 6 service providers/decision makers. The interviews were analyzed using qualitative inductive analysis and coded for themes based on recurring issues. Results: Limited understanding of mental health and illness among refugees, stigma, and the need for culturally competent care were noted by the service providers. System navigation, language, unemployment and safety of family members left behind in Syria were the main concerns of the refugee women. While there are many programs available for refugee women in Winnipeg, lack of collaboration and coordination among providers was identified. Conclusions: This study recommends that service providers use resources developed by UNHCR and Canadian physicians in providing culturally competent care, decision makers take leadership roles in implementing better collaboration among agencies, employers be open in hiring refugees and everyone in the society ensures that the refugee women feel welcomed and included.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".