Are Canada’s Older Adults Aging Successfully? An Analysis of the Canadian Community Health Survey
Bibliographic record
Abstract
Defining successful aging (SA) has been a topic of debate amongst researchers since Rowe and Kahn introduced the topic of “usual” versus “successful” aging in the late 1980s. Researchers have criticized their biomedical model of successful aging, which has been used as an unofficial gold standard in determining whether one has aged usually or successfully. This perspective focuses on having high physical and mental functional capacities, being void of disease or disease-related disability, and having an active engagement with life, and is considered too narrow in its focus and lacking subjective interpretations of aging. Using the 2011-2012 version of the Canadian Community Health Survey with Canadian adults aged 60 years and older (N = 15,846), 15.9% of respondents were aging successfully, 81% were aging moderately successfully, and 3.1% were aging unsuccessfully with the biomedical model based on Rowe and Kahn’s (1987; 1997; 1998) three postulates of aging success. Using the psychosocial criteria based on a review of SA literature, 18.3% of respondents were aging successfully, 66.1% were aging moderately successfully, and 15.6% were aging unsuccessfully. Using the integrative criteria, which combined both the biomedical and psychosocial perspectives, 28.9% of respondents were aging successfully, 55.5% were aging moderately successfully, and 15.6% were aging unsuccessfully. Results from the integrated model are depicted on a continuum that illustrates the difference in aging success based on a combination of predictors unique to each perspective of SA. This model has the potential to demonstrate that those individuals who may not be aging successfully in biomedical terms (attainment), may otherwise be aging successfully in psychosocial terms (adaptation).
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.016 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 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".