THE HEALTH TRAJECTORY OF OLDER IMMIGRANTS IN CANADA: FINDINGS FROM THE CANADIAN LONGITUDINAL STUDY ON AGING
Bibliographic record
Abstract
Abstract In Canada, older immigrants constitute about one-third of aging Canadians, and the population size of older immigrants is projected to keep growing. The health and wellbeing of older immigrants are critical to Canada’s healthcare and social services systems. This study intends to examine the health trajectory of older immigrants in Canada over ten years (2011 to 2021) using three waves of data from the Canadian Longitudinal Study on Aging based on Linear Mixed Models. The data analysis unit contains a total of 21,480 older adults (aged 65 years and older), including 4,248 immigrants and 17,232 Canada-born. The results indicate that older immigrants report better physical health (fewer chronic diseases and better physical capability) than Canada-born older adults but experience a greater decline in physical health over ten years. When it comes to mental health, older immigrants and Canada-born older adults report similar levels of depression and loneliness, and the change of mental health is parallel over time. In addition, older immigrants report significantly lower levels of social wellbeing (e.g., social support and social participation), and a greater decline in perceived social support and social participation. The findings from this study enrich the “healthy immigrant effect” with older immigrants’ health situation in Canada over ten years. The findings also emphasize the need to support older immigrants in Canada to maintain social wellbeing and enhance the positive impacts of social determinants of health during the aging process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".