Educational Interventions for Implementation of Arthritis Clinical Practice Guidelines in Primary Care: Effects on Health Professional Behavior
Bibliographic record
Abstract
OBJECTIVE: The dissemination and adoption of clinical practice guidelines (CPG) has been suggested as one method for improving arthritis care delivery. This article provides a review and synthesis of studies evaluating the influence of educational programs designed to implement CPG for osteoarthritis (OA) and rheumatoid arthritis (RA) in primary care. METHODS: A systematic literature search was conducted to identify relevant educational interventions that reported behavioral outcomes that ensured actual knowledge utilization in primary care. A standardized approach was used to assess the quality of the individual studies and a modified version of the Philadelphia Panel methodology allowed for grading of studies based on strength of design, clinical relevance, and statistical significance. RESULTS: The search identified 485 articles; 7 studies were selected for review. In OA, peer facilitated workshops with nurse case-management support for patients decreased the number of referrals to orthopedics by 23%, and educational outreach by trained physicians improved prescribing of analgesics. Interprofessional peer facilitated workshops were successful in increasing referrals to rehabilitation services for people with OA and RA. CONCLUSION: There was sparse literature on educational programs for the implementation of arthritis CPG in the primary care environment. Future studies are needed to evaluate which specific organizational, provider, patient, and system level factors influence the uptake of arthritis CPG in primary care.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".