Exploratory study on the discourse of an interdisciplinary team on workers: trajectories during a return-to-work programme
Bibliographic record
Abstract
Purpose: Based on the viewpoint of an interdisciplinary team, this exploratory study aimed to identify different types of trajectories followed by workers with musculoskeletal disorders and the factors contributing to them.Methods: The research design used a single-case study in which the main unit of analysis was an interdisciplinary work team. This team discussed eighteen workers’ progression during a work rehabilitation programme. Analytical methods were based on phenomenology. All team discussions were audiotaped and transcribed, and two researchers completed the content analysis.Results: Four types of trajectories emerged: (1) return-to-work trajectories without obstacles; (2) return-to-work trajectories with obstacles; (3) non-return-to-work trajectories with episodes of progression; and (4) non-return-to-work trajectories without progression. Moreover, three outlines emerged from the data analysis: (1) the worker’s compliance with the programme; (2) the way the worker coped with exposure to work; and (3) stakeholder collaboration. The results of this study also suggested that the absence of a single consistent message among participating health professionals could create confusion for workers and pose a major impediment to the resumption of their activities.Conclusion: The results underscore, for clinicians, the complexity in managing this type of chronic work rehabilitation population, related to both the worker and the worker’s interactions with the stakeholders. Also, this study casts light on the non-linear work rehabilitation processes of individuals with prolonged disabilities of musculoskeletal origin, as described by an interdisciplinary team.
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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 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".