Effects of health status on work exit and absenteeism among the older working population in China: a secondary analysis of a cohort sample
Bibliographic record
Abstract
OBJECTIVES: To analyse the effects of health status on work exit and absenteeism among the older working population in China. DESIGN: Secondary analysis of a cohort sample. SETTING AND PARTICIPANTS: Community samples who engaged in either agricultural or non-agriculture work or both in the 2011 wave of the China Health and Retirement Longitudinal Study (CHARLS) and whose age was 45-55 years for women or 45-60 years for men in the 2013 wave. OUTCOME MEASURES: Work exit and number of absent workdays due to health problems in 2013. To address the problems of measurement error of self-rated health status, we used disability condition, number of chronic diseases and functional limitation to construct an index of health. We divided the sample into four groups according to gender and work types (farmers who conducted any agricultural work in 2011 vs non-farmers who conducted non-agricultural work only) and conducted analyses separately. RESULTS: Farmers (11.0% for women and 4.9% for men) were less likely to exit from work than non-farmers (18.5% and 12.0%, respectively) but took more absent workdays (16.6 days for women and 15.0 days for men) than non-farmers (5.6 and 4.9). Poor health status in 2011 was significantly associated with the work exit in 2013 of female and male farmers but not non-farmers. Older workers (except female non-farmers) with persistently poor health or recent health deterioration over time were significantly more likely to stop working or missed more workdays than those with persistently good health. CONCLUSIONS: Poor 2-year lagged health predicts work exit for both male and female farmers, and increases the absent work days in all older working population. Persistently poor health or recent health deterioration over time has detrimental impact on labour market in terms of work exit and absenteeism among all older Chinese workers except for female non-farmers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".