The Role of Job Control and Job Demands in Becoming Physically Active during the COVID-19 Pandemic: A Three-Wave Longitudinal Study
Bibliographic record
Abstract
Organizational studies suggest that certain psychosocial working conditions are liable to foster positive health outcomes, such as engaging in leisure-time physical activities. However, the psychosocial factors contributing to this improvement remain unexplored, particularly in the workplace and in the context of the decline observed in the physical activity level of the population worldwide. The objective of the study was to examine whether exposure to different combinations of psychosocial working conditions during the COVID-19 pandemic predicts the probability of becoming physically active among Quebec workers. Job demands, job control, and physical activity were assessed three times during the first year of the pandemic via an online questionnaire among physically inactive workers (n = 440). Logistic regression analyses were conducted to examine the associations between various combinations of psychosocial risks and physical activity. A total of 117 participants became physically active during the study. After controlling for covariates, active jobs increased the odds of becoming physically active, compared to high-strain jobs (OR = 2.57 (95% CI 1.13 to 5.87)). Having a highly demanding job may not negatively impact physical activity if workers have enough job control to achieve the required tasks.
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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 | 0.002 |
| 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 | 0.001 |
| 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".