Equitable and Empowered Access to Primary Care: Challenges faced by Immigrant Nigerian Women in Canada.
Bibliographic record
Abstract
Background: Statistics Canada’s 2021 Census reported that 19.5% of recent African immigration (January 2016 to May 2021) to Canada comprised of Nigerian immigrants. Despite the country’s unique tribes and cultures, Nigerians are often lumped in with other nations when studying health patterns amongst African immigrants. As more immigrants settle in Canada, understanding the unique challenges they face accessing primary healthcare will help address their needs. A sample of Nigerian immigrant women were interviewed through focus group discussions to understand their experiences and challenges accessing Canada’s primary healthcare. Method: Using a community-based participatory design, eight focus groups were conducted among 41 first-generation Nigerian immigrant women on Zoom. The focus groups were conducted and transcribed in English. A thematic analysis following Braun and Clarke’s (2006) guideline was then performed to identify emerging themes and patterns. Results: The focus group discussions noted five major themes, following the socioecological model. Intrapersonal level barriers revealed financial pressures due to un/employment and past traumas with physicians. Interpersonal level barriers included difficulties communicating with physicians and dismissal of their health concerns. Institutional level barriers included wait times for primary and emergency services, unavailable female physicians, and limited cultural competency from providers. Community level barriers included religious and cultural stigmas towards mental and sexual healthcare, and policy level barriers revealed extreme costs of prescriptions, dental, and vision care. Conclusion: Nigerian immigrant women are a unique group as they do not report many language barriers due to speaking English back home, which differs from studies conducted on other immigrants. However, they experience many barriers and their large presence in Canada necessitates an awareness of their health needs in local, provincial, and federal health policy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".