The development of a participant questionnaire to assess continuing medical education presentations
Bibliographic record
Abstract
OBJECTIVE: Feedback on presentation skills is important for developing skilled educators, but often this feedback is based on evaluation tools that have been developed with little concern for psychometric issues or for how the information will be used for feedback. The purpose of this study was to develop a reliable participant questionnaire to assess the quality of continuing medical education (CME) presentations and to provide presenters with feedback. DESIGN: The questionnaire was developed using an iterative approach, with doctors as raters, and tested during a variety of CME presentations. The resulting questionnaire consists of 9 items rated on a 7-point Likert scale. The psychometric analysis reported in this paper was completed using data from grand rounds presentations. RESULTS: Psychometric analysis, based on 319 evaluations from 17 presentations (average of 19 evaluations/presentation), revealed a high level of reliability (0.91), indicating that the items met a reasonable standard and that the raters were discriminating between the quality of the presentations adequately. CONCLUSION: This 9-item, participant questionnaire provides a reliable measure of the quality of CME presentations, while also providing presenters with useful feedback. Further studies will investigate if this instrument can be used to assess other CME formats and how best to provide feedback to presenters.
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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 | 0.050 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".