Implementing arthritis clinical practice guidelines in primary care
Bibliographic record
Abstract
BACKGROUND: Multi-faceted interventions are among the strongest methods for changing provider behavior. AIMS: This paper reports the design, implementation and process evaluation of an educational program to disseminate clinical practice guidelines (CPGs) on the management of rheumatoid arthritis (RA) and osteoarthritis (OA) in primary care. METHODS: Organizations were invited to participate in inter-professional workshops on OA and RA followed by six months of activities to support the delivery of care in the community. Confidence in ability to manage arthritis was assessed at baseline using a 10 point numerical rating scale. Qualitative assessments were done immediately and 3-12 months post workshop. RESULTS: 646 multidisciplinary providers from 216 organizations attended one of 30 workshops. Providers (n = 584) reported moderate confidence in managing arthritis: family physicians: mean: SD = 7.46(1.42), n = 145; nurse practitioners: 6.10(1.84), n = 73; other health care professionals: 5.23(2.83), n = 389. Participants identified team learning, the opportunity to network and the involvement of trained patient educators as strong features of the workshops. At follow-up, participants indicated the greatest impact of the program was on collaborative care (83%) and patient self-management (79%). CONCLUSIONS: Qualitative results suggest that inter-professional learning may be a successful strategy for improving the delivery of collaborative arthritis care and supporting patient self-management.
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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 | 0.042 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".