The values underlying team decision-making in work rehabilitation for musculoskeletal disorders
Bibliographic record
Abstract
PURPOSE: This paper presents the results of a qualitative study on the values underlying the decision-making process of an interdisciplinary team working in a work rehabilitation facility of a Québec teaching hospital. METHODS: In order to document the values underlying the decision-making process, a single case observational study was conducted. Interdisciplinary team weekly discussions on ongoing cases of 22 workers absent from work due to musculoskeletal disorders were videotaped. All discourses were transcribed and analyzed following an inductive and iterative approach. The values identified were validated by feedback from team members. RESULTS: Ten common decision values emerged from the data: (1) team unity and credibility, (2) collaboration with stakeholders, (3) worker's internal motivation, (4) worker's adherence to the program, (5) worker's reactivation, (6) single message, (7) reassurance, (8) graded intervention, (9) pain management and (10) return to work as a therapy. The analysis of these values led to the design of a model describing interrelations between them. CONCLUSIONS: This study throws light on some mechanisms underlying the decisions made by the team and determining its action. This improves understanding of the actions taken by an interdisciplinary team in work rehabilitation and may facilitate knowledge transfer in the training of other teams.
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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 | 0.027 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".