Latent Classification Analysis of Leisure Activities and Their Impact on ADL, IADL and Cognitive Ability of Older Adults Based on CLHLS (2008–2018)
Bibliographic record
Abstract
This study aimed to research the trajectory of leisure activity and the health status of older adults and analyze the effects of leisure activity on the health status of older adults. Based on the longitudinal data of CLHLS (2008-2018), the latent growth curve model (LGCM) was used; we found that the leisure activities (LA), activities of daily living (ADL) ability, instrumental activities of daily living (IADL) ability, and cognitive ability (COG) of older adults show a nonlinear downward trend over time. Furthermore, the panel binary regression analysis is used to find that leisure activities have significant inhibitory effects on ADL disorder, IADL disorder, and cognitive impairment in the older population. In addition, by using latent profile analysis (LPA), the older population is classified into three groups according to the homogeneity of the older adults' choice of leisure activities, namely the types of relaxation, entertainment, and intellectual-learning, respectively. Based on the classification results, the analysis of one-way ANOVA shows that the rates of ADL disorder, IADL disorder, and cognitive impairment of older adults with different types are significantly different. Moreover, the inhibitory effect of leisure activities on the rate of ADL disorder, IADL disorder, and cognitive impairment of older adults is more significant in the middle-aged and high-aged groups. Therefore, older adults should be encouraged to increase leisure activities, especially those who are middle-aged and high-aged.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".