Le rôle modérateur de l'activité physique dans la relation entre le modèle demande-latitude et le présentéisme
Bibliographic record
Abstract
Objective The aim of this study was to investigate the moderating role of physical activity in the relationship between job demands, job control and presenteeism. Methods The data were collected from a population-based web panel of 1,450 workers in Quebec. The Job Demand-Control (JDC) model was used to dichotomize job demands and control. To test the moderating role of physical activity, a preliminary analysis was carried out in which the relationship between job demands, job control and presenteeism was stratified by physical activity level (active/inactive). Then, a latent moderation was modeled in which physical activity was hypothesized to moderate the relationships between JDC categories (active, passive, low-strain, high-strain) and presenteeism. Results Physical activity significantly moderated the relationship (i.e., lower presenteeism) for the active job and passive job categories. Lower presenteeism was also observed for high-strain job but not at a significant level. No moderation was observed for low-strain job. Conclusion Physical activity, which can be considered as a personal resource, is likely to reduce presenteeism for employees in an active or passive job. The results of this study suggest the value of incorporating an individual resource such as physical activity as a moderating variable in the relationship between work characteristics and presenteeism.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".