Comparison of self-rated and objective successful ageing in an international cohort
Bibliographic record
Abstract
ABSTRACT Understanding predictors of successful ageing is essential to policy development promoting quality-of-life of an ageing population. Initial models precluded successful ageing in the presence of chronic disease/functional disability; however, this is discrepant with self-reported successful ageing. Indicators of social, psychological and physical health in 1,735 people aged 65–74, living in Canada, Columbia, Brazil or Albania, were analysed in the International Mobility in Ageing Study. Multiple logistic regression analysis was performed to estimate the change in self-rated successful ageing in relation to physical health, depression, social connectedness, resilience and site, while controlling for age, gender and income sufficiency. Sixty-five per cent of participants self-rated as ageing successfully; however, this was significantly different across sites (p < 0.0005, range 17–85%) and gender (p = 0.019). Using objective measures, 6 per cent were classified as ‘successful’, with significant variability amongst sites (p < 0.0005, range 0–12%). Subjective successful ageing was associated with fewer (not absence of) chronic diseases, absence of depression and less dysfunction in activities of daily living, but not with objective measures of physical dysfunction. Social connectedness and resilience also aligned with self-rated successful ageing. Traditional definitions of objective successful ageing are likely too restrictive, and thus, do not approximate self-rated successful ageing. International differences suggest that site could be a surrogate for variables other than physical/mental health and social engagement.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".