Successful Aging among Immigrant and Canadian-Born Older Adults: Findings from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background: Few studies in Canada have focused on the relationship between immigrant status and successful aging. The concept of successful aging used in this study includes the ability to accomplish both activities of daily living (ADLs) and instrumental activities of daily living (IADLs), freedom from mental illness, memory problems and disabling chronic pain, adequate social support and older adults’ self-reported happiness and subjective perception of their physical health, mental health and aging process as good. Methods: The present study analyzed the first two waves of data from the comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA). The sample includes 7651 respondents aged 60+ at time 2, of whom 1446 respondents were immigrants. Bivariate and multivariable binary logistic regression analyses were conducted. Results: Canadian-born older adults had a slightly higher prevalence and age-sex adjusted odds of achieving successful aging than their immigrant counterparts (aOR = 1.18, 95% CI: 1.04, 1.34, p < 0.001). After adjusting for 18 additional factors, immigrant status remained statistically significant (aOR = 1.24, 95% CI: 1.09, 1.41, p < 0.001). Significant baseline factors associated with successful aging among immigrants included being younger, having higher income, being married, not being obese, never smoking, engaging in moderate or strenuous physical activities, not having sleeping problems and being free of heart disease or arthritis. Conclusions: Immigrant older adults had a lower prevalence of successful aging than their Canadian-born peers. Further research could investigate whether policies and interventions supporting older immigrants and promoting a healthy lifestyle enhance older adults achieve successful aging in later life.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".