Psychosocial Risks and Subjective Well-Being in the Canadian Workplace
Bibliographic record
Abstract
This article puts forward a new typology of workers, based on an enhanced set of indicators of psychosocial risks and well-being, and examines the character traits associated with each class membership. This article innovates by simultaneously taking into account how hostile behaviours, poor working conditions and employment precariousness are associated with different subjective measures of well-being. This study uses a person-centered approach by conducting latent class analysis on a representative sample of 5,867 Canadian employees. Six distinct clusters are revealed: “heavily suffering”, “unfulfilled precarious”, “unhealthy stressed”, “untroubled harassed”, “optimistic precarious” and “not exposed”. This article thus shows that it is not harassment or lack of social benefits per se that affect workers’ well-being. It demonstrates that workers’ well-being deteriorates only when hostile behaviours/conflicts and poor working/employment conditions overlap. Binary logistic regression analyses reveal that, controlling for other worker characteristics, this typology of workers is related to work ethic and resilience. The results suggest two key trends: overlapping exposure to precariousness, procedural injustice and poor prospects for career advancement reduces hard work ethic, while overlapping exposure to hostile behaviour/conflicts and competition reduces resilience.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".