Mental health at work: From defining to solving the problem. Solving the problem: Preventing stress in the workplace
Bibliographic record
Abstract
Cet ouvrage est issu des travaux effectués sur la prévention des problèmes de santé psychologique au travail (Brun, J.-P. et al. 2002), et s'adresse à celles et ceux qui souhaitent comprendre cette problématique et s'outiller afin de mieux la prévenir. L'itinéraire proposé comporte trois étapes: l'ampleur du problème, les causes et la prévention. Ces documents permettront aux travailleurs et aux organisations de mieux faire face à ce problème qui est la cause principale de l'augmentation de l'absentéisme au travail. De la même sérieScope of the problem: How workplace stress is shown What causes the problem? The sources of workplace stress Version française disponibleL'ampleur du problème : l'expression du stress au travailLes causes du problème : les sources de stress au travailFaire cesser le problème : la prévention du stress au travail Abstract The series entitled “Mental Health at Work... From Defining to Solving the Problem” is published by the Chair in Occupational Health and Safety Management at Université Laval, Québec, Canada. This series is intended for persons who are involved in occupational health and safety (OHS) and especially mental health at work. From the same seriesScope of the problem: How workplace stress is shown What causes the problem? The sources of workplace stress French version available>L'ampleur du problème : l'expression du stress au travailLes causes du problème : les sources de stress au travailFaire cesser le problème : la prévention du stress au travail
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".