Prescription for Education: Development, Evaluation, and Implementation of a Successful Interprofessional Education Program for Adults with Inflammatory Arthritis
Bibliographic record
Abstract
OBJECTIVE: To assess the feasibility of recruitment and standardize care delivery for an interprofessional program for inflammatory arthritis education (Prescription for Education, or RxEd), and to explore outcomes relevant to arthritis patient education. METHODS: A patient-based needs assessment and ongoing patient feedback guided program development. An interprofessional team was involved in developing program content and delivering and adapting the program to patient needs. A quasiexperimental, waitlisted control with crossover design was used to evaluate the program. Data were collected at baseline, immediately following intervention, at 6 months (when the crossover control group received intervention), and at 1 year. Self-report measures included demographics, disorder-related data, Arthritis Self-efficacy Scale, arthritis knowledge, coping efficacy, and illness intrusiveness. Analysis included baseline comparisons and longitudinal trends; direct between-group comparison at 6 months; and generalized estimating equations (GEE) analysis to evaluate the main effect of the intervention on the primary outcome (arthritis self-efficacy) and secondary outcomes. RESULTS: Program modifications based on patient input made recruitment possible. Forty-two persons participated (including 19 controls), with 93% followup at 1 year. Comparison of change shows moderate effect sizes (standardized effect size 0.5 to 0.7). GEE analysis showed significant main effect, before to after the program, in both groups for primary outcome (arthritis self-efficacy) and most secondary outcomes. CONCLUSION: Program feasibility was dependent on patient feedback. Our pilot study provides evidence that the RxEd program is feasible and improves arthritis self-efficacy and other outcomes.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".