Immigration, sex/gender, and patterns of access to primary care in Canada
Bibliographic record
Abstract
Background: Primary care provides entry to the Canadian health care system, subsequent coordination, and integration of all levels of care. People who have immigrated to Canada may experience difficulties accessing primary care, but gaps in conceptualization and operationalization of “access” has resulted in conflicting results and uncertain policy implications. How the interaction of immigration and sex/gender impacts patterns of access to primary care has also been understudied. Objectives: The study examines how patterns of access to primary care differ between immigration groups and how sex/gender and immigration interact to shape patterns of access. Methods: I used 2015-2018 Canadian Community Health Survey Data to describe patterns of access to primary care among adult residents of Canada (age 18+), categorizing variables related to primary care access by stages of the Levesque framework including perception and desire for care, healthcare seeking, reaching and utilization. I compared patterns of access between recent immigrants (people in Canada for less than 10 years, including both temporary migrants and permanent immigrants), long-term immigrants (in Canada 10+ years), and non-immigrants (people who were born in Canada). I used logistic regression models to calculate adjusted and unadjusted odds estimates for each variable related to primary care, and to explore interaction effects of sex/gender and immigration. Results: Patterns of access among recent immigrants significantly differed from long-term and non-immigrants with respect to perception and desire for care, seeking and utilization. Similar patterns were observed between long-term and non-immigrants except at the utilization stage. Interaction effects of gender and immigration were more pronounced in earlier stages of health care seeking but not reaching nor utilization. Conclusion: The Lévesque framework of health services use was important in highlighting differences among immigration groups along the whole process of obtaining care. Interventions focused on improving approachability and acceptability of services and addressing gendered barriers, especially among recent male immigrants, are important to improve access to primary care.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".