Applying a reflexive framework to evaluate a communication skills curriculum
Bibliographic record
Abstract
Lawrence Cheung Department of Medicine, University of Alberta, Edmonton, AB, Canada Abstract: After creating and delivering an educational curriculum, medical educators must ultimately evaluate the effectiveness of the implemented curriculum. Seasoned educators can benefit from using an established framework to help them structure a thorough, complete curricular evaluation; however, novice educators may have difficulty in transforming the concept of evaluation into a concrete process. The RUFDATA (Reasons and purpose, Uses, Focus, Data and evidence, Audience, Timing, and Agency) framework is one such paradigm. It is a well-recognized tool consisting of a reflexive framework that can guide medical educators to evaluate their own medical education curriculum. Just as important, it enables medical educators to reflect on the reasons behind the evaluation. This insight, in turn, can foster a spirit of evaluation, thus helping to ingrain it into the local educational culture. By using the evaluation of our communication skills curriculum as an example, this article describes how educators can apply the RUFDATA framework to evaluate their own curriculum. Keywords: curriculum evaluation, RUFDATA, communication skills, course evaluation, reflexive evaluation
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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 | 0.358 | 0.318 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".