Unpaid overtime and mental health in the Canadian working population
Bibliographic record
Abstract
BACKGROUND: Unpaid overtime-describing a situation where extra hours are worked but not paid for-is a common feature of the labor market that, together with other forms of wage theft, costs workers billions of dollars annually. In this study, we examine the association between unpaid overtime and mental health in the Canadian working population. We also assess the relative strength of that association by comparing it against those of other broadly recognized work stressors. METHODS: Data were drawn from a survey administered to a heterogeneous sample of workers in Canada (n = 3691). Generalized linear models quantified associations between unpaid overtime, stress, and burnout, distinguishing between moderate (1-5) and excessive (6 or more) hours of unpaid overtime. RESULTS: Unpaid overtime was associated with higher levels of stress and burnout. Relative to those working no unpaid overtime, men working excessive unpaid overtime were 85% more likely to report stress (prevalence ratios [PR]: 1.85, 95% confidence interval [CI]: 1.26-2.72) and 84% more likely to report burnout (PR: 1.84, 95% CI: 1.34-2.54), while women working excessive unpaid overtime were 90% more likely to report stress (PR: 1.90, 95% CI: 1.32-2.75) and 52% more likely to report burnout (PR: 1.52; 95% CI: 1.12-2.06). The association of excessive unpaid overtime with mental health was comparable in magnitude to that of shift work and low job control. CONCLUSIONS: Unpaid overtime may present a significant challenge to the mental health of working people, highlighting the potential role of wage theft as a neglected occupational health hazard.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".