Moving toward an integrated prevention approach for mental health at work: Promoting workers’ involvement through concrete actions
Bibliographic record
Abstract
BACKGROUND: Work-related mental health problems are a primary cause of disability and lead to the absence of 500,000 workers each week in Canada. There is a growing body of literature suggesting integrated approaches of prevention are necessary to improve mental health at work. The involvement of numerous stakeholders inclusive of government agents, employers, and workers is recommended. However, only minimal information is available to suggest actions workers may adopt toward an integrated approach of prevention to improve mental health at work. OBJECTIVE: The aim of the study was to identify behaviors workers may adopt to foster mental health at work. METHODS: Following a descriptive qualitative research design, semi-structured interviews were conducted with researchers, professionals, and workers. Data were analyzed using a template analysis strategy. RESULTS: A total of 49 concrete behaviors were identified, grouped into ten sub-themes, and three broad themes. These main themes identify those behaviors that appear to be useful throughout the prevention continuum: 1) adopting a reflexive practice, 2) acting for one's own mental health, and 3) acting for mental health of others. CONCLUSIONS: In harmony with the integrated prevention approach, this study offers a framework to organize workers' concrete actions contributing to mental health.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".