People with arthritis and their families in rehabilitation, care and research
Bibliographic record
Abstract
PURPOSE OF REVIEW: People with arthritis play major roles in treatment and research. This review summarises the current knowledge on tools for enhancing shared-decision making in arthritis care; individual and family involvement in rehabilitation; and the consumer's role in arthritis research. RECENT FINDINGS: There are discrepancies in the use of appropriate arthritis treatment. To facilitate evidence-informed treatment choices, a number of decision aids have been developed. A recent systematic review concluded that decision aids could improve the shared-decision making process in a variety of diseases; but only one clinical trial was found on a musculoskeletal condition (back surgery). The evidence on family member participation in arthritis education programs is mixed, partly due to a lack of content specifically targeting family members in some studies. Finally, people with arthritis are playing important roles as collaborators in research. Early experience indicates a mutually beneficial relationship for both the individual and researchers. SUMMARY: This review offers three recommendations: First, further clinical trials are needed to test the effectiveness of decision aids in arthritis management. Second, education programs involving strong social support training for family members may improve client outcomes. Third, we encourage further studies to examine the experiences and challenges of people living with arthritis when participating as research partners.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".