Development of a Multimodal, Physiotherapist-Led, Vocational Intervention for People with Inflammatory Arthritis and Reduced Work Ability: A Mixed-Methods Design Study
Bibliographic record
Abstract
PURPOSE: Work ability of people with rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is reduced, but underexamined as a clinical treatment target. The evidence on vocational interventions indicates that delivery by a single healthcare professional (HCP) may be beneficial. Physiotherapist (PT)-led interventions have potential because PTs are most commonly consulted by RA/axSpA patients in the Netherlands. The aim was to develop a PT-led, vocational intervention for people with RA/axSpA and reduced work ability. METHODS: Mixed-methods design based on the Medical Research Council (MRC) framework for developing and evaluating complex interventions, combining a rapid literature review and six group meetings with: patient representatives (n = 6 and 10), PTs (n = 12), (occupational) HCPs (n = 9), researchers (n = 6) and a feasibility test in patients (n = 4) and PTs (n = 4). RESULTS: An intervention was developed and evaluated. Patient representatives emphasized the importance of PTs' expertise in rheumatic diseases and work ability. The potential for PTs to support patients was confirmed by PTs and HCPs. The feasibility test confirmed adequate feasibility and underlined necessity of training PTs in delivery. The final intervention comprised work-focussed modalities integrated into conventional PT treatment (10-21 sessions over 12 months), including a personalized work-roadmap to guide patients to other professionals, exercise therapy, patient education and optional modalities. CONCLUSION: A mixed-methods design with stakeholder involvement produced a PT-led, vocational intervention for people with RA/axSpA and reduced work ability, tested for feasibility and ready for effectiveness evaluation.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".