Predicted factors for older Taiwanese to be healthy octogenarians: Results of an 18‐year national cohort study
Bibliographic record
Abstract
AIM: To identify factors that predict the 62-69 years old Taiwanese to be healthy octogenarians. METHODS: We analyzed the 1989 (baseline), and 2003 and 2007 (end-point) datasets of the Taiwan Longitudinal Survey on Aging, a national cohort study. A total of 1977 participants aged 62-69 years at baseline were tracked for 14-18 years. The outcome measure was "being healthy octogenarians", defined as participants who were aged ≥80 years, free from activities of daily living dependency, depressive symptoms or cognitive impairment, and able to provide social support. A logistic regression model was used to identify the predictors. RESULTS: The results showed that higher educational level, conjugal living, absence of smoking or betel quid chewing, moderate alcohol drinking, routine physical activity, more leisure activities, no hypertension, no diabetes, sleeping well and satisfied with economic condition were the positive predictors for becoming a healthy octogenarian. CONCLUSIONS: Using a multidimensional criterion, the present study identified a list of factors in predicting older Taiwanese becoming healthy octogenarians. The findings highlight the need to identify potential factors for various populations. Many of the predictors are modifiable factors. The present results would be valuable for planning effective health promotion strategies to achieve healthy aging for older adults. Geriatr Gerontol Int 2017; 17: 2579-2585.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".