The Development and Evaluation of a Vocational Rehabilitation Training Programme for Rheumatology Occupational Therapists
Bibliographic record
Abstract
People with inflammatory arthritis rapidly develop work disability, yet there is limited provision of vocational rehabilitation (VR) in rheumatology departments. As part of a randomized, controlled trial, ten occupational therapists (OTs) were surveyed to identify their current VR provision and training needs. As a result, a VR training course for OTs was developed which included both taught and self-directed learning. The course included: employment and health and safety legislation, work assessment and practical application of ergonomic principles at work. Pre-, immediately post- and two months post-training, the ten OTs completed a questionnaire about their VR knowledge and confidence On completion, they reported a significant increase (p < 0.01)in their knowledge and confidence when delivering vocational rehabilitation. They rated the course as very or extremely relevant, although seven recommended more practical sessions. The preference for practical sessions was highlighted, in that the aspects they felt most beneficial were role-playing assessments and sharing ideas through discussion and presentations. In conclusion, the course was considered effective in increasing both knowledge and confidence in using VR as an intervention, but, due to time constraints within the working day, some of the self-directed learning should be incorporated into the training days.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".