A crisis in workplace mental health injuries... And in work itself
Bibliographic record
Abstract
A crisis in workplace mental health injuries... And in work itself Dr Mélanie Dufour-Poirier, Associate Professor at the University of Montreal’s School of Industrial Relations, discusses opportunities to safeguard employees’ mental health injuries and wellbeing through union involvement. Well before the COVID-19 pandemic and the widespread requirement for telework, mental health injuries in the workplace (e.g., chronic stress, anxiety, depression, burnout and, in the worst cases, suicide) increased throughout the world. The acute rise in these problems is now considered the epiphenomenon of a global social and societal crisis, not to mention a major public health concern. That every year in Canada, on average, five hundred thousand workers are not at work due to mental health issues is testament to this. From that perspective, more and more research (including ours) shows the existence of a close correlation between how work is conceived, designed, organized and carried out and the prevalence of mental health problems in workplaces.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".