Original Article Continuing Medical Education-Driven Skills Acquisition and Impact on Improved Patient Outcomes in Family Practice Setting
Bibliographic record
Abstract
Background: An abundance of educational theory, design, and delivery of continuing medical education (CME) learning interventions, including their impact on learners, are described in the health and social sciences literature. However, establishing a direct correlation between the acquisition of new skills by learners and patient outcomes as a result of a planned CME learning intervention has been difficult to demonstrate. Methods: The learning intervention described here tested the impact of an injection skillsacquisition program for family physicians treating osteoarthritis of the knee by measuring patient outcomes using the pain and function subscales of the Western Ontario and McMaster (WOMAC) 3.0 osteoarthritis index, a standardized and fully validated patient-centered outcome measurement. It was hypothesized that patients of family physicians who participated in this skills-acquisition CME program would benefit from treatment administered by their physician during the time between injection skills acquisition to 6 weeks post-injection. Inclusion of a validated health status measure administered pre- and post-injection in addition to more traditional faculty and participant program evaluations was deemed necessary to test this hypothesis. Rheumatology, orthopedic surgery, and family medicine specialists from across
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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 | 0.001 | 0.010 |
| 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) | 0.010 | 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".