Feasibility evaluation of a return-to-work program for workers with common mental disorders: Stakeholders’ perspectives
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the feasibility of a newly developed return-to-work program for workers with common mental disorders from the perspective of stakeholders (insurers, employers, unions, and workers). METHODS: We used a sequential mixed design. First, we conducted a survey to evaluate the levels of stakeholder agreement with the program's feasibility. Second, we conducted a number of independent, homogeneous-group discussions or individual interviews to deepen stakeholders' reflections and allow co-construction of a shared perspective of the program's feasibility. RESULTS: Overall, the stakeholders (insurers (n = 6), employers (n = 7), unions (n = 8), and workers (n = 3)), agreed partly to totally with the feasibility of the specific/intermediate objectives, components/tasks, and duration of the components. They identified obstacles that could hinder program implementation. These obstacles pertained mainly to employers' contexts, e.g., difficulty/impossibility of offering job accommodations. They also proposed facilitators to counteract most of these obstacles. Diverging views were found regarding both the role of union representatives and health professionals in the program, and for the duration of the components. CONCLUSION: Overall, the program was perceived as feasible to implement, provided that the potential factors discussed are taken into account. The next step will be to evaluate its implementation in real practice settings.
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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 | 0.046 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".