FACTORS ASSOCIATED WITH SOCIAL ACTIVITY PARTICIPATION AMONG MIDDLE-AGED AND OLDER CHINESE IN CHINA
Bibliographic record
Abstract
Research from Western samples has linked participation in social, leisure, and other productive activities in later life with better physical and mental health. In China, social activity participation is an understudied, yet important topic. To measure and investigate social activity participation in China, ten items were included in the first wave (2011) of the nationally representative Chinese Health and Retirement Longitudinal Study (CHARLS). In this study, we aimed to (1) classify the dimensions of social activity among middle-aged and older Chinese adults through an exploratory factor analysis (EFA), and (2) identify the socio-demographic characteristics associated with each factor through a confirmatory factor analysis (CFA). Results from the EFA identified four dimensions of social activity, including informal volunteering (helping others without organizational affiliation), leisure (interacting with friends), formal volunteering and learning (charity work through an organization and lifelong learning), and technology use. The indices suggested that the hypothesized model fit the data adequately (1, n=16,224) =13.50, p=.26; RMSEA=.01; CFI=.99; TLI=.99; SRMR=.02. CFA findings showed that socio-demographic factors including age, education, marital status, and residential region were significantly associated with the informal volunteering factor, but not the other three factors. Respondents who were older (β=.06, p <.01), higher educated (β=.04, p < .01), married (β =.14, p <. 001), and living in an urban region (β =.13, p <. 001) were most likely to volunteer informally. In conclusion, this study establishes a framework for classifying dimensions of social activity participation that can be used in future research to explore cross-cultural comparisons.
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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 | 0.002 |
| 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 | 0.001 |
| 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".