Logic models for the Therapeutic Return-to-Work Program as adapted for common mental disorders: A guide for health professionals
Bibliographic record
Abstract
BACKGROUND: Workplace interventions are recommended for workers with common mental disorders, but knowledge of their action mechanisms and operationalization remains limited. The Therapeutic Return-to-Work Program, developed for workers with musculoskeletal disorders, is recommended for common mental disorders. OBJECTIVE: Our objective was to adapt this program's logic models to common mental disorders. METHODS: A program logic analysis was conducted using a literature review and a two-phase group consensus method. We submitted a preliminary adapted version of the program's logic models and two questionnaires to health professional experts who participated in two group sessions, ultimately to produce the final version of the models. RESULTS: We consulted 86 publications. The health professional experts (N = 7) had overall mean agreement scores of respectively 4.10/5 and 3.89/5 for questions on the program's theoretical and operational models. The final version of the logic models adapted for common mental disorders included four specific and 15 intermediate objectives, three main components, one optional component, four key processes, and 44 tasks. CONCLUSION: The adapted logic models for the Therapeutic Return-to-Work Program show the relevance of the original objectives and components for common mental disorders. The next step will involve evaluating its feasibility with other stakeholders (insurers, employers, unions, workers).
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".