Trajectories of ageing well among older Australians: a 16-year longitudinal study
Bibliographic record
Abstract
ABSTRACT In this study we used individual differences concepts and analyses to examine whether older people achieve different ageing-well states universally or whether there are identifiable key groups that achieve them to different extents. The data used in the modelling were from a prospective 16-year longitudinal study of 1,000 older Australians. We examined predictors of trajectories for ageing well using self-rated health, psychological wellbeing and independence in daily living as joint indicators of ageing well in people aged over 65 years at baseline. We used group-trajectory modelling and multivariate regression to identify characteristics predicting ‘ageing well’. The results showed three distinct and sizeable ageing trajectory groups: (a) ‘stable-good ageing well’ (classified as ageing well in all longitudinal study waves; which was achieved by 30.2% of women and 28.0% of men); (b) ‘initially ageing well then deteriorating’ (50.5% women and 47.6% men); and (c) ‘stable-poor’ (not ageing well in any wave; 19.3% women and 24.4% men). Significant gender differences were found in membership in different ageing-well states. In the stable-poor groups there were 103/533 females which was significantly lower than 114/467 men ( z -statistic = −2.6, p = 0.005); women had a ‘zero’ probability of progressing to a better ageing-well classification in later years, whilst males had a one-in-five probability of actually improving. Robust final state outcome predictors at baseline were lower age and fewer medical conditions for both genders; restful sleep and Australian-born for women; and good nutrition, decreased strain, non-smoker and good social support for men. These results support that ageing-well trajectories are influenced by modifiable factors. Findings will assist better targeting of health-promoting activities for older people.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".