Chronic health conditions, healthcare experience and life satisfaction among immigrant and native-born women in Canada
Bibliographic record
Abstract
Purpose To compare chronic health status, utilization of healthcare services and life satisfaction among immigrant women and their Canadian counterparts. Design/methodology/approach A secondary analysis of national data from the Canadian Community Health Survey (CCHS), 2015–2016 was conducted. The survey data included 109,659 cases. Given the research question, only female cases were selected, which resulted in a final sample of 52,560 cases. Data analysis was conducted using multiple methods, including logistic regression and linear regression. Findings Recent and established immigrant women were healthier than native-born Canadian women. While the Healthy Immigrant Effect (HIE) was evident among immigrant women, some characteristics related to ethnic origin and/or unhealthy dietary habits may deteriorate immigrant women's health in the long term. Immigrant women and non-immigrant women with chronic illnesses were both more likely to increase their use of the healthcare system. Notably, the present study did not find evidence that immigrant women under-utilized Canada's healthcare system. However, the findings showed that chronic health issues were more likely to decrease women's life satisfaction. Originality/value This analysis contributes to the understanding of immigrant women's acculturation by comparing types of chronic illnesses, healthcare visits, and life satisfaction between immigrant women and their Canadian counterparts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".