Patient involvement in the development of a handbook for moderate rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Self-management is a key recommendation for people with rheumatoid arthritis (RA). Educational materials may support self-management, and increasingly patients are becoming involved with the development of these materials. The TITRATE trial compares the effectiveness of intensive management to standard care in patients with moderate RA across England. As part of the intensive management intervention, participants are given a handbook. AIM AND OBJECTIVES: The aim of this study was to develop a handbook to support the intensive management. The objectives were to: (i) involve patients in the identification of relevant information for inclusion in the TITRATE handbook; (ii) ensure the content of the handbook is acceptable and accessible. DESIGN: We held an audio-taped workshop with RA patients. The transcript of the workshop was analysed using thematic content analysis. RESULTS: Five main themes were identified as follows: 'rheumatoid arthritis treatment, perceptions of rheumatoid arthritis, the importance of individualized goals, benefits of self-management and the patient handbook'. Feedback from the workshop was incorporated into the handbook, and patients' anonymous testimonies were added. CONCLUSION: This study demonstrates that patient contribution to the development of educational material to support intensive management of RA is both feasible and valuable. A qualitative evaluation of the use and impact of the handbook with patients and practitioners is planned on completion of the TITRATE trial.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".