Gender-Based Violence in a Migration Context: Health Impacts and Barriers to Healthcare Access and Help Seeking for Migrant and Refugee Women in Canada
Bibliographic record
Abstract
This article focuses on the health impacts of the gender-based violence (GBV) experienced by migrant and refugee women (MRW) survivors in their migration/settlement journeys in Canada, and their challenges in accessing healthcare. Adopting a feminist and intersectional lens, I draw upon qualitative in-depth interviews with 48 migrant women conducted between 2020 and 2022. GBV is a frequent experience in the migration and (re)settlement journey and has wide-ranging and cross-secting emotional-psychological, socio-economic, physical, as well as sexual and reproductive health consequences which, in turn, impact settlement and integration and may increase vulnerability to further GBV as a result. Drawing upon a “social determinants of health” approach, I aim to understand the workings of barriers to healthcare access and help seeking for MRW survivors of GBV in Canada. The social determinants of health involve structural (e.g., legal, financial, linguistic, knowledge, healthcare access) barriers, mediated by gender, intersecting with various positionalities and identities. GBV unambiguously impacts on the health and well-being of all survivors, but the extent of harm varies significantly depending on the intersections of positions and identities of survivors. The migration context entails unique barriers to MRW help seeking and healthcare access as well as aggravates the impacts of other barriers on MRW. My objective is to show how GBV affects the health status of MRW survivors in Canada in the specific context of healthcare access and help-seeking barriers MRW face, conceptualized as risk factors for reproducing GBV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".