Influence of discipline of provider and model of care on an arthritis educational intervention in primary care
Bibliographic record
Abstract
OBJECTIVE: To identify both provider and organizational characteristics that predicted outcomes following an educational intervention (9-hour workshop and followup reinforcement activities) developed to improve the management of arthritis in primary care. METHODS: Providers completed a survey at baseline and at 6 months postworkshop, including a case scenario for early rheumatoid arthritis. Providers were asked how they would manage the case and their responses were coded to calculate a best practice score, ranging from 0-7. Two-level hierarchical linear modeling was used to determine which of the measured provider and organizational factors predicted best practice scores at followup. RESULTS: A total of 275 multidisciplinary providers from 131 organizations completed both baseline and followup surveys. Best practice scores increased by 17% (P < 0.01); however, the mean score at 6-month followup remained relatively low (2.68). Significant predictors of best practice scores at followup were discipline of provider and model of primary care in which they worked (P < 0.05), adjusting for baseline practice scores and clustering of providers within organizations. Physicians, nurse practitioners, and rehabilitation therapists scored higher than nurses, students, and other health care providers (P < 0.01). Physician networks scored significantly lower than providers from multidisciplinary-oriented models of care (P = 0.02). CONCLUSION: These results have implications for the education of health professionals and the design of models of care to enhance arthritis care delivery.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".