A prospective comparison of telemedicine versus in-person delivery of an interprofessional education program for adults with inflammatory arthritis
Bibliographic record
Abstract
Introduction We evaluated two modes of delivery of an inflammatory arthritis education program (“Prescription for Education” (RxEd)) in improving arthritis self-efficacy and other secondary outcomes. Methods We used a non-randomized, pre-post design to compare videoconferencing (R, remote using telemedicine) versus local (I, in-person) delivery of the program. Data were collected at baseline (T 1 ), immediately following RxEd (T 2 ), and at six months (T 3 ). Self-report questionnaires served as the data collection tool. Measures included demographics, disorder-related, Arthritis Self-Efficacy Scale (SE), previous knowledge (Arthritis Community Research and Evaluation Unit (ACREU) rheumatoid arthritis knowledge questionnaire), coping efficacy, Illness Intrusiveness, and Effective Consumer Scale. Analysis included: baseline comparisons and longitudinal trends (R vs I groups); direct between-group comparisons; and Generalized Estimating Equations (GEE) analysis. Results A total of 123 persons attended the program (I: n = 36; R: n = 87) and 111 completed the baseline questionnaire (T 1 ), with follow-up completed by 95% ( n = 117) at T 2 and 62% ( n = 76) at T 3 . No significant baseline differences were found across patient characteristics and outcome measures. Both groups (R and I) showed immediate effect (improved arthritis SE, mean change (95% confidence interval (CI)): R 1.07 (0.67, 1.48); I 1.48 (0.74, 2.23)) after the program that diminished over six months (mean change (95% CI): R 0.45 (−0.1, 0.1); I 0.73 (−0.25, 1.7)). For each of the secondary outcomes, both groups showed similar trends for improvement (mean change scores (95% CI)) over time. GEE analysis did not show any meaningful differences between groups (R vs I) over time. Discussion Improvements in arthritis self-efficacy and secondary outcomes displayed similar trends for I and R participant groups.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".