The Impact of Occupational Therapy on the <scp>Self‐Management</scp> of Rheumatoid Arthritis: A Mixed Methods Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of occupational therapy (OT) on the self-management of function, pain, fatigue, and lived experience for people living with rheumatoid arthritis (RA). METHODS: Five databases and gray literature were searched up to June 30, 2022. Three reviewers screened titles and abstracts, with two independently extracting and assessing full texts using the Cochrane risk of bias (quantitative) and Critical Appraisal Skills Programme (qualitative) tools to assess study quality. Studies were categorized into four intervention types. Grading of Recommendations, Assessment, Development and Evaluations (GRADE) (quantitative) and GRADE- Confidence in Evidence from Reviews of Qualitative research (qualitative) were used to assess the quality of evidence for each intervention type. RESULTS: Of 39 eligible papers, 29 were quantitative (n = 2,029), 4 qualitative (n = 50), and 6 mixed methods (n = 896). Good evidence supports patient education and behavior change programs for improving pain and function, particularly group sessions of joint protection education, but these do not translate to long-term improvements for RA (>24 months). Comprehensive OT had mixed evidence (limited to home OT and an arthritis gloves program), whereas limited evidence was available for qualitative insights, splints and assistive devices, and self-management for fatigue. CONCLUSION: Although patient education is promising for self-managing RA, no strong evidence was found to support OT programs for self-managing fatigue or patient experience and long-term effectiveness. More research is required on lived experience, and the long-term efficacy of self-management approaches incorporating OT, particularly timing programs to meet the individual's conditional needs (i.e., early or established RA) to build on the few studies to date.
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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 | 0.031 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".