“I go because I don’t have an alternative”: Older Portuguese Immigrant Women’s Experiences Accessing and Using Health Care Services in Toronto
Bibliographic record
Abstract
Existing research indicates that health inequalities are complex and impact groups differently. Group-specific settlement experiences affect how health care services are accessed and used. Of these groups, older immigrant women face exacerbated challenges and disadvantages related to the compounding negative influence of gender and aging (Wang, Guruge & Montana, 2019). Expanding on existing literature in the field of immigrant health, this study focuses on the Portuguese community in Toronto and their interactions with the health care system. Specifically, this study explores the ethnicized, gendered, and class-specific settlement experiences of Portuguese immigrant women and how these experiences influence the way health care services are accessed and used in older age. This qualitative study is based on interviews with older Portuguese immigrant women living in Toronto, as well as Portuguese-speaking health and social service providers who serve the Portuguese community in Toronto. Guided by the theoretical approach of intersectionality, this study found that the cumulative, lifelong settlement challenges that participants experienced stretched well into their older years and negatively influenced their access to and use of health care services. In addition, this study also found that study participants unexpectedly identified systemic barriers such as issues related to patriarchy, gender relations, and violence within their private lives in addition to discrimination based on gender, class, and ethnicity while engaging in accessing and using health care services.
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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 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".