Exposition aux risques psychosociaux et détresse psychologique des travailleurs québécois selon la taille d'entreprise
Bibliographic record
Abstract
A Ab bs st tr ra ac ct t Workplaces impact significantly on employees' psychological health.Even if small and medium sized enterprises (SME) contribute largely as employers in Quebec, mental health and psychosocial work environment issues remain misunderstood.Using a representative sample of Quebec' working population of 4608 workers, this study aims to compare the psychosocial work environment and psychological health in Quebec organizations based on their size.Furthermore, three managers working full time in SMEs were interviewed.Results suggest that even though psychological distress does not vary with organization's size, individuals working for very small enterprises are less likely to be exposed to effort-reward imbalance and to job strain than those from small, medium and large organizations.Also, the relationship between psychosocial risks and psychological distress differs according size.Practitioners and intervention researchers should take into account of these differences and adapt organizational interventions depending on the organization's size and its particular needs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".