Exploring the healthcare experiences of African immigrant women in Winnipeg, Manitoba
Bibliographic record
Abstract
This dissertation explores the healthcare experiences of African immigrant women living in Winnipeg, Manitoba. It seeks to understand the barriers and facilitators encountered and their perception of the system. Informed by intersectionality, it aims to highlight how the participants' intersecting identities and the social context shape their experiences. In individual interviews and focus groups, 27 women who were recent immigrants to Canada, discussed their interactions with healthcare providers and the healthcare system. Many women face barriers navigating the healthcare system, accessing services, and communicating with providers. In contrast, positive healthcare interactions were characterised by being actively involved in their care, and feeling seen and heard. In order to overcome these barriers, concerted efforts are needed at the levels of local community organisations, healthcare providers and healthcare policy. In a reflective methodology paper I discuss the complex experience of conducting qualitative research within one's own community and the challenges that arose from interviewing Black women as a Black woman. I draw on interview and focus group transcripts from the primary study, existing literature and my own reflections. I found that while insider status and universalization helped facilitate conversations with participants, challenges were also encountered. A reluctance to speak about negative experiences and hesitancy to name racism hindered deeper exploration of their experiences. These manuscripts are complementary in the information they provide and contribute to the limited literature on African immigrant women in Canada. Although they use different methods and have differing objectives, they are informed by a feminist standpoint methodology and take an intersectional approach that privileges the voices of Black women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".