Evaluation of an Inter-Professional Educational Intervention to Improve the Use of Arthritis Best Practices in Primary Care
Bibliographic record
Abstract
OBJECTIVE: To describe the evaluation of a community-based continuing health education program designed to improve the management of rheumatoid arthritis (RA) and osteoarthritis (OA), and to examine the results by discipline. METHODS: The Getting a Grip on Arthritis(©) program was based on clinical practice guidelines adapted for the primary care environment (best practices). The program consisted of an accredited inter-professional workshop and 6 months of activities to reinforce the learning. Analyses compared best practice scores derived from responses to 3 standardized case scenarios (early and late RA; moderate knee OA) at baseline and 6 months post-workshop using the ACREU Primary Care Survey. RESULTS: In total, 553 primary care providers (nurses/licensed practical nurses 30.9%, rehabilitation professionals 22.5%, physicians 22.5%, nurse practitioners 10.9%, other healthcare providers/non-clinical staff/students 13.1%) attended one of 27 workshops across Canada; 275 (49.7%) completed followup surveys. Best practice scores varied by discipline at baseline (p < 0.05) and improved for all 3 case scenarios, with nurse practitioners and rehabilitation therapists improving the most (p ≤ 0.05). CONCLUSION: Results suggest that inter-professional education may be an effective method for dissemination of guidelines and has potential to improve the delivery of arthritis care, particularly when nurse practitioners and rehabilitation therapists are involved in the care of patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.009 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".