[A typology of mental health comorbidity in workplaces: results from the SALVEO study].
Bibliographic record
Abstract
Objectives The aim of this study consists in describing the nature of mental health comorbidity among workers. Precisely, we seek to examine the presence of concomitant associations between burnout (cynicism, emotional exhaustion, professionnal inefficacy) and psychoactive substance use (heavy episodic drinking, above low-risk drinking guidelines, and psychotropic drug use).Methods The SALVEO study is based on a cross-sectional sample of 1966 workers from the province of Québec, Canada. Latent class analyses were performed in order to identify typical patterns corresponding to distinct forms of mental health comorbidity in the data. Multinomial logistic regressions on latent classes were performed using covariables pertaining to work and non-work domains and workers' individual characteristics.Results Four typical patterns in mental health comorbidity were found: 1- "Severe burnout and psychotropic drug use"; 2- "At risk drinking and cynicism"; 3- "Emotional exhaustion and professional inefficacy"; and 4- "Relatively healthy state". Of all four patterns, the "Severe burnout and psychotropic drug use" pattern presented the highest number of cumulative risks (environmental and individual).Conclusion Comorbidity in mental health is a matter of importance in workplaces from the province of Québec. The severity in the different patterns of mental health comorbidity expressed a cumulative effect of risk factors from the work and non-work domains, as well as individual characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".