Psychosocial Risks and Subjective Well-Being in the Canadian Workplace
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it