Does participation in interdisciplinary work rehabilitation programme influence return to work obstacles and predictive factors?
Bibliographic record
Abstract
PURPOSE: Musculoskeletal disorders evolve into long-term work disabilities in--10% of work-injury cases. From a prevention perspective, screening for predictive factors and obstacles associated with long-term work disability appear to offer a promising avenue for work rehabilitation. However, knowledge of the factors at play during the chronic phase remains limited. This study aims to explore the presence of a relationship between the predictive factors and obstacles identified at the time of admission to an interdisciplinary work rehabilitation programme and return to work upon completion of the programme, in individuals with a long-term work disability. METHOD: A descriptive correlational study involving 222 individuals assessed using the Work Disability Diagnostic Interview and who participated in the PREVICAP work rehabilitation programme. RESULTS: The general model accurately predicts the work status of 77% of the participants. Seven to nine factors were found to be associated with return to work in each model produced (3). Those factors were mainly psychosocial and work-related in nature and differ according to gender. Unexpectedly, certain obstacles observed at the time of admission to the programme appear to have a protective effect and thus promote participants' return to work. CONCLUSION: The results obtained support the hypothesis that screening for predictive factors and obstacles at the time of admission of a work rehabilitation programme for individuals with a long-term work disability allows for more effective intervention regarding these factors, and in all likelihood, promotes return to work.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".