A Continuum of Successful Aging: The Impact of a Biomedical and Psychosocial Approach
Bibliographic record
Abstract
Defining successful aging (SA) has been debated since Rowe and Kahn introduced "usual" versus "successful" aging in the late 1980s. This perspective requires high physical and mental functional capacities, being void of disease or disease-related disability, and having an active engagement with life. Researchers have considered this model narrow in focus and lacking subjective interpretations of aging. Using the 2011-2012 Canadian Community Health Survey with Canadians aged 60 years and older (n = 15,846), it was found that 15.9% were aging successfully, 81% were aging moderately successfully, and 3.1% were aging unsuccessfully with a biomedical model (attainment) based on Rowe and Kahn's three postulates of aging success. Using psychosocial (adaptation) criteria based on a review of SA literature, it was found that 18.3% of respondents were aging successfully, 66.1% were aging moderately successfully, and 15.6% were aging unsuccessfully. Using integrative criteria combining both biomedical and psychosocial perspectives, 28.9% of respondents were aging successfully, 55.5% were aging moderately successfully, and 15.6% were aging unsuccessfully. The integrated model results are depicted on a continuum illustrating the differences in aging success based on a combination of predictors unique to each SA perspective. This model can potentially demonstrate the interplay between biomedical and psychosocial outcomes in aging success.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".