Understanding the Influence of Immigration on the Aging Experience in Canada
Bibliographic record
Abstract
This study explores how immigration affects the aging experience in Canada by examining the role of social determinants of health (SDOHs) in shaping physical and mental health outcomes among older adults. With Canada’s aging population and a growing proportion of immigrants, understanding these influences is vital for developing equitable health and social policies. Using data from a national health survey, we applied structural equation modeling to analyze relationships between immigrant status, SDOHs, chronic physical and mental health conditions, and overall quality of life. The analysis included over 26,000 participants and revealed that immigrants were more likely to experience adverse social conditions, which were linked to higher rates of chronic illnesses and mental health challenges. Physical health problems also contributed to increased mental health burden. Machine learning methods were used to further assess how social and health factors impact quality of life, highlighting important predictors specific to immigrant populations. These findings demonstrate that social and economic factors significantly influence the health and well-being of aging immigrants. The results emphasize the need for policies and interventions that address social inequalities to promote healthier aging in diverse communities. Overall, this research advances knowledge on the complex interplay between immigration, social determinants, and aging, offering valuable insights to support healthier and more inclusive aging experiences in Canada.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".