People getting a grip on arthritis II: An innovative strategy to implement clinical practice guidelines for rheumatoid arthritis and osteoarthritis patients through Facebook
Bibliographic record
Abstract
Objective: The purpose of the study is to determine if an updated online evidence-based educational programme delivered through Facebook is effective in improving the knowledge, skills, and self-efficacy of patients with arthritis in relation to evidence-based self-management rehabilitation interventions for osteoarthritis (OA) and rheumatoid arthritis (RA). Methods: Adult patients (>18 years old) with self-reported OA or RA were eligible for the study. One-hundred-and-ten participants were recruited from the general public and different arthritis patient organizations throughout Canada. Eleven participants were selected to participate in focus groups to select effective self-management strategies for OA and RA according to level of implementation burden. Ninety-nine participants were then selected to participate in the online Facebook intervention which included a ‘group’ web page providing case-based video clips on how to apply the selected self-management interventions. Over a three-month period participants were asked to complete three online questionnaires regarding their previous knowledge, intention to use/actual use of the self-management strategies, self-efficacy and confidence in managing their condition. Results: Knowledge acquisition scores improved among OA and RA participants with a mean difference of 1.8 ( p < 0.01) when compared from baseline to immediate post-intervention. At three months post-intervention, almost all self-management strategies were successful with participants following through on their intention to use the self-management strategies; however, statistically significant results were only demonstrated for ‘aquatic jogging’ ( p < 0.05) and ‘yoga’ ( p < 0.05) among OA participants, and ‘aquatic therapy’ ( p < 0.01) among RA participants. Self-efficacy was maintained from immediate post-intervention to three months follow-up, and confidence improved as the study progressed. Conclusions: This online programme can provide patient organization representatives with the opportunity to learn about and integrate evidence-based self-management strategies for OA and RA in their daily lives, to increase their awareness of useful community resources, and support their efforts to disseminate the information to others with arthritis.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".