Getting a Grip on Arthritis Online: Responses of rural/remote primary care providers to a web-based continuing medical education programme
Bibliographic record
Abstract
INTRODUCTION: Physicians are often challenged with accessing relevant up-to-date arthritis information to enable the delivery of optimal care. An online continuing medical education programme to disseminate arthritis clinical practice guidelines (CPGs) was developed to address this issue. METHODS: Online learning modules were developed for osteoarthritis (OA) and rheumatoid arthritis (RA) using published CPGs adapted for primary care (best practices), input from subject matter experts and a needs assessment. The programme was piloted in two rural/remote areas of Canada. Knowledge of best practice guidelines was measured before, immediately after completion of the modules and at 3-month follow-up by assigning one point for each appropriate best practice applied to a hypothetical case scenario. Points were then summed into a total best practice score. RESULTS: Participants represented various professions in primary care, including family physicians, physiotherapists, occupational therapists and nurses (n = 89) and demonstrated significant improvements in total best practice scores immediately following completion of the modules (OA pre = 2.8/10, post = 3.8/10, P < 0.01; RA pre = 3.9/12, post = 4.6/12, P < 0.01). The response rate at 3 months was too small for analysis. CONCLUSIONS: With knowledge gained from the online modules, participants were able to apply a greater number of best practices to OA and RA hypothetical case scenarios. The online programme has demonstrated that it can provide some of the information rural/remote primary care providers need to deliver optimal care; however, further research is needed to determine whether these results translate into changes in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".