Challenges in Evaluating an Arthritis Self-management Program for People with Hip and Knee Osteoarthritis in Real-world Clinical Settings
Bibliographic record
Abstract
OBJECTIVE: To evaluate the influence of a 6-week Arthritis Self-Management Program (ASMP) on health-related quality of life (HRQOL) and self-management skills in clinical settings. METHODS: Individuals with hip or knee osteoarthritis referred to orthopedic surgeons or rheumatologists at 6 hospitals in Victoria, Australia, were recruited. In a randomized controlled trial, participants received the Stanford ASMP and self-help book (intervention) or book only (control). Assessments included the Assessment of Quality of Life instrument (AQoL; range -0.04 to 1.00) and Health Education Impact Questionnaire (heiQ; range 1-6) at baseline and up to 12 months. The primary outcome was HRQOL at 12 months (assessed using the AQoL). RESULTS: Recruitment was concluded early due to persistent challenges including infrequent referrals and patient inability or disinterest in participating. Of 1125 individuals screened, only 120 were randomized (control, n = 62; intervention, n = 58). Seven ASMP were conducted while 18 scheduled ASMP were cancelled. Forty-four of 58 intervention group participants received the intervention as allocated (76%); all control group participants were sent the book (100%). Ninety-four participants (78%) completed 12-month assessments (control, 90%; intervention, 66%). There was no difference in HRQOL at 12 months (adjusted mean difference -0.02, 95% CI -0.09 to 0.05). At 6 weeks, the intervention group reported higher heiQ skill and technique acquisition scores (adjusted mean difference 0.29, 95% CI 0.04 to 0.55); however, this dissipated by 3 months. CONCLUSION: Significant challenges hampered this evaluation of the ASMP. The observed lack of enthusiasm from potential referrers and patients raises doubts about the practicality of this intervention in real-world settings. (ANZCTR Clinical Trials Registry no. ACTRN12606000174583).
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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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".