Do Changes in Job Control Predict Differences in Health Status? Results From a Longitudinal National Survey of Canadians
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of changes in job control on health behaviors, psychological distress and health status. METHODS: Using a path analysis model, we examined the effects of change in job control over a 4-year period on levels of physical activity, smoking, and psychological distress; and on self-rated health over an additional 2 years, among a representative sample of 2221 Canadians. RESULTS: Over the 4-year period, 280 respondents reported decreases in job control, and 256 reported increases in job control. Health at baseline was not associated with the likelihood of changes in job control. We found a graded relationship between change in job control and levels of physical activity and psychological distress over a 4-year period; and levels of self-rated health over a 6-year period, with positive change in job control associated in higher levels of physical activity and self-rated health and lower levels of distress. CONCLUSIONS: The results of this study suggest that both level of job control and changes in job control have direct and indirect effects on health status over time. Future research should focus on developing precise measures of work exposures, and examine differences between changes in job control due to only changes in perceptions and changes due to work redesign.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".