Successful Aging in Canada: Findings from the Canadian Longitudinal Study on Aging (CLSA)
Bibliographic record
Abstract
Background: Successful aging is the desire of older adults and those who have devoted their lives to the care of older adults. Few studies in Canada have focused on the associations between (1) immigrant status; (2) marital trajectories; (3) social participation, and successful aging. This three-paper dissertation introduced an expanded definition of successful aging included 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 (Ho et al., 2022; Ho et al., 2023). Methods: The first two waves of data from the comprehensive cohort of the Canadian Longitudinal Study on Aging (CLSA) were analyzed. The final samples included 7,600+ respondents defined as “aging successfully” at baseline and were 60 years or older at time 2. Bivariate and multivariable binary logistic regression analyses were conducted. Results: The study found that (1) older immigrants, (2) older adults who were never married or had experienced widowhood, separation and divorce in later life; and (3) older adults who did not engage in volunteer or charity work, and recreational activities had a significantly lower odds of achieving successful aging than their peers. Other significant baseline factors associated with successful aging included being younger, female sex, having higher income, being married, not being obese, not smoking, engaging in moderate or strenuous physical activities, not having sleeping problems and being free of heart disease or arthritis. Conclusions: The study contributed to the body of literature on successful aging by providing an expanded definition of successful aging and studying older immigrants, older adults with different trajectories of marital status, and older adults who have participated in social activities. The findings provided a strong argument that some older adults might achieve successful aging through engaging in activities that promote physical, psychological, mental, social, and self-rated wellness. Practical implications and future research directions were discussed.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".