2.11-P13Understanding the needs of refugee women in navigating the Canadian healthcare system
Bibliographic record
Abstract
Migration and refugee crises are increasing globally due to war, conflict and natural disasters leaving women and children more vulnerable and having a negative impact on their health and well-being. Women refugees account for about half of the 244 million migrants and nearly half of the 19.6 million refugees worldwide (UN General Assembly, 2016). It is important to explore the healthcare experiences of refugees settling in Canada to understand how the structure and operation of the healthcare system meets their health needs. Although some studies exist on the difficulty women refugees face in accessing and utilizing healthcare services, there is a paucity of research on how women refugees navigate the Canadian healthcare system and the gaps that exist between their expectations and actual experiences. This study seeks to understand the healthcare needs of refugee women and identify strategies that can help improve their familiarity with, access to, and navigation and utilization of the Canadian healthcare system. A qualitative research approach informed by a intersectionality feminist framework will be used to understand the intersecting factors that influence how women refugees access healthcare in Southern Alberta, Canada. The inclusion criteria for selection using purposive sampling will be refugee women between the ages of 18-49 years living in Alberta for about six months to five years. Data will be collected through four (4) focus group discussions and eight (8) in-depth individual interviews. The study will employ an inductive thematic analysis approach to explore the collective voices of women’s narrative stories or perspectives about healthcare services in Canada. The study aims to answer the question – what do refugee women really need and how does the Canadian healthcare system fulfil these needs? It is expected that the study findings will provide reliable information and challenge stakeholders’ interest and policy making for refugee women’s health needs which is necessary for their settlement.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 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".