Getting a Grip on Arthritis: an educational intervention for the diagnosis and treatment of arthritis in primary care.
Bibliographic record
Abstract
OBJECTIVE: To evaluate a community-based educational intervention designed to improve the diagnosis and treatment of arthritis in primary care. METHODS: The educational intervention, entitled "Getting a Grip on Arthritis", consisted of a 2-day workshop and followup reinforcement activities for healthcare providers (providers) and was supported by a toolkit of written materials for providers and clients. The content of the intervention was designed around 10 arthritis best practices derived from published arthritis guidelines. Five community health centers (CHC) participated as intervention sites and 2 as control sites. Intervention impact was determined through a mailed survey to clients with arthritis. Primary outcome analysis compared responses to questions about arthritis best practices between intervention and control sites at baseline and followup. RESULTS: The workshop was attended by 21 multidisciplinary providers from intervention CHC. At baseline, 423 of 624 eligible and consenting clients completed the survey and 376 of 593 completed the followup survey. At followup clients in the intervention group reported significantly higher referrals to The Arthritis Society therapy program, and were more often provided information on type of arthritis, medications and their side effects, disease management strategies, and arthritis community resources. CONCLUSION: This demonstration project is one of the first to show changes in the management of arthritis in a primary care setting. This project has recently received funding from Health Canada's Primary Health Care Transition Fund for implementation across Canada and is expected to provide a template for use in other chronic diseases.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".