Exploring mental healthcare needs and challenges of Syrian refugee women in Winnipeg
Bibliographic record
Abstract
The UN Refugee Agency reports that 70.8 million people have been forcibly displaced at the end of 2018. Canada has a long history of accepting refugees and is the second largest resettlement country in the world. While refugees arrive to safety and protection in Canada, language, socio-economic barriers, and cultural differences contribute to struggles. The effects of war, family separation, displacement and long travel may pose more stresses on women, particularly when overlapping with pregnancy, childbirth, and caring for children. Considering the recent influx of refugees, particularly from Syria, it is important to understand how Manitoba is addressing the mental health needs of Syrian refugee women. The objectives of this study were to 1) conduct a literature review on the mental healthcare needs and barriers in accessing services by refugee women in the Canadian context; 2) explore the perspectives of Syrian Refugee women on how they experience mental health and the healthcare system in Winnipeg; 3) explore the perspectives of service providers and decision-makers on how refugee women experience mental health and access healthcare services in Winnipeg; and 4) provide policy recommendations to improve mental healthcare services in Manitoba based on the research findings. Semi-structured interviews were conducted with Syrian refugee women and service providers/decision makers in Winnipeg. The data were analyzed using qualitative thematic analysis and coded for themes aided by NVivo 12 software. The findings presented in two broad subject areas – barriers in accessing mental healthcare services and strategies to improve mental healthcare services for refugee women in Winnipeg. Most cited barriers by refugee women were language, weather, employment and income level, stigma and system navigation. The service providers mentioned stigma, and lack of resources to provide culturally competent care. Collaboration among agencies, leadership at all levels, and education for both refugee women and service providers were also mentioned. This study recommends that service providers use guidelines developed by UNHCR in providing culturally competent care, decision makers take leadership roles in implementing better collaboration among agencies, the employers be open in hiring refugees, and educational initiatives for the broader society to ensure that refugees 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".