Prediction of subjective age, desired age, and age satisfaction in older adults: Do some health dimensions contribute more than others?
Bibliographic record
Abstract
Of all the variables that have been shown to contribute to subjective age, health variables typically explain the greatest proportion of variance, with poorer health related to feeling older than one's chronological age. Despite the significant contribution of health to subjective age, little research has explored the relative importance of different dimensions of health to subjective age. The primary aim of the present study was to examine the relative importance of various physical, mental, social, and emotional dimensions of health, as well as satisfaction with health, to measures of subjective age, desired age, and satisfaction with age in a sample of 875 older men and women. The results indicated that: (1) certain combinations of health dimensions and satisfaction with health accounted for relatively large proportions (20—36%) of the variance in subjective age and satisfaction with age but not desired age; (2) subjective age and satisfaction with age were explained by different combinations of health dimensions; and (3) the health predictors of subjective age and satisfaction with age differed somewhat for men and women and for young-old and old-old groups.
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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 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".