The effect of warning signals from health check-ups on modifiable lifestyle risk factors: evidence from mandatory health check-ups for employees in Japan
Bibliographic record
Abstract
Health check-ups provide information on disease risk for individuals. It is assumed that such negative health information will lead to the adoption of healthier lifestyles. However, the relationship between the information provided by health check-ups and subsequent lifestyle modifications remains unclear. This study investigates whether warning signals that people receive after health check-ups lead to modified smoking and drinking behaviours over 10 years after the check-ups, using a longitudinal nationwide survey of middle-aged people conducted from 2005 to 2018 in Japan. The panel nature of the data enabled me to control for unobserved individual heterogeneity as individual fixed-effects. The results show that negative health information provided by check-ups reduces smoking and drinking. The effects were found to persist more than 10 years after the check-ups. It was also found that older, more educated, and higher-income individuals make significant reductions in these behaviours after receiving warnings. These findings suggest that negative health information from health check-ups may steer lifestyles in a healthier direction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".