Co-construction: A key to preventing mental health injuries in occupational settings
Bibliographic record
Abstract
Co-construction: A key to preventing mental health injuries in occupational settings Dr Mélanie Dufour-Poirier from the Université de Montréal and Dr Jean-Paul Dautel from the Université du Québec en Outaouais outline a new approach to preventing workplace mental health injuries and improving wellbeing. In Canada, it is estimated that mental health-related costs total approximately $50bn annually. In the province of Quebec, close to one out of two workers has experienced psychological distress since the onset of the COVID-19 pandemic. Amongst its pathogens are the porosity of working time, isolation, competition among colleagues, and the demand for short-term profitability. New - more stressful - forms of work organization, including telework, have emerged, bringing with them excessive workloads, increases in the pace of work, growing job flexibility, precarious contract employment, and job insecurity. These issues have put workplace mental health back at the centre of the debate in Quebec. More generally, the crisis has called for a rethinking of management methods for better upstream protection of mental health, ideally through a collective and participatory approach that involves all the actors in a working community rather than one that aims at protecting against and treating such mental health injuries on the level of the individual, as is the current tendency. It is an issue of the utmost importance for unions, a subject we discussed in a recent article (Open Access Government, April 2024).
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.017 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".