[Mental Health and Return/Integration to Work: International Concertation on Solutions and Priority Actions].
Bibliographic record
Abstract
Introduction and Objective As part of the conference Crossed Perspectives on Mental Health and Return to Work, held in Montreal in 2021, a white paper was produced to highlight 10 solutions aimed at promoting mental health in the workplace and facilitating return to work (RTW). Examples include actions such as formalizing the RTW process with common guidelines for personalized follow-up, training managers on mental health prevention and employees support during their RTW, and generating evidence-based data to better understand and promote sustainable and healthy return to work after medical leave. Three years later, during a roundtable discussion at the inaugural conference of the International Community of Practice on Mental Health and Work (CoP-SMT in French), the objective was to revisit these solutions by engaging stakeholders involved in employee support and company representatives. Method During the round table, eight individuals holding executive positions in RTW services or management roles in public and private organizations from Belgium, France, and Quebec were invited to select one or two solutions from the white paper. They were asked to share which solutions they had implemented or wished to implement in their work contexts. Additionally, participants were encouraged to identify one or two priority actions they deemed essential for discussion at the next conference in 2027. A synthesis of their responses are presented as the outcome of this exercise. Results and Conclusions The results provide insight into current practices, challenges, and needs identified by the experts consulted, helping to outline research directions to support the implementation of practices/initiatives across different countries. These reflections also consider emerging responsibilities and evolving roles of various stakeholders involved. Notably, the concept of workplace mental health should extend beyond merely addressing psychosocial risk factors by incorporating protective factors as well.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.005 | 0.016 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".