The Lived Experiences of Immigrant Canadian Women with the Healthcare System
Bibliographic record
Abstract
Immigrants to Canada report better health status than the Canadian-born population when they first arrive in Canada, a phenomenon called the Healthy Immigrant Effect. However, by the fourth year after immigration, immigrants report a health status that is worse than that of the Canadian-born population. Visible minority immigrant women report the largest deterioration in health. The purpose of this qualitative study was to explore the lived experiences of visible minority immigrant women with encounters with the Canadian healthcare system to examine the multiplicative impact of gender, ethnicity, and immigration on their health. This phenomenological study, guided by Crenshaw's feminist intersectionality framework, explored the perspectives of a purposive sample of 8 immigrant women in Ottawa, Canada, about their encounters with the healthcare system. Data were collected through individual interviews. These data were inductively coded and subjected to thematic analysis following the process outlined by Smith et al. for interpretative phenomenological analysis. Key findings of the study revealed that immigrant women define health more holistically and have expectations of the encounters with healthcare that are not met due to barriers that impact them accessing healthcare services, experiencing healthcare services, and following the recommended options. The positive social change implications of this study include recommendations for public health to consider immigration and racism as determinants of health; and for Health Canada to undertake system-level lines of inquiry to shed light on the ways structural discrimination and racism have had an impact on immigrant women's social and health trajectory.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".