Defining quality criteria for online continuing medical education modules using modified nominal group technique *
Bibliographic record
Abstract
INTRODUCTION: The rapid increase in the use of the Internet for continuing education by physicians suggests the need to define quality criteria for accredited online modules. METHODS: Continuing medical education (CME) directors from Canadian medical schools and academic researchers participated in a consensus process, Modified Nominal Group Technique, to develop agreement on the most important quality criteria to guide module development. Rankings were compared to responses to a survey of a subset of Canadian Medical Association (CMA) members. RESULTS: A list of 17 items was developed, of which 10 were deemed by experts to be important and 7 were considered secondary. A quality module would: be needs-based; presented in a clinical format; utilize evidence-based information; permit interaction with content and experts; facilitate and attempt to document practice change; be accessible for later review; and include a robust course evaluation. There was less agreement among CMA members on criteria ranking, with consensus on ranking reached on only 12 of 17 items. In contrast to experts, members agreed that the need to assess performance change as a result of an educational experience was not important. DISCUSSION: This project identified 10 quality criteria for accredited online CME modules that representatives of Canadian organizations involved in continuing education believe should be taken into account when developing learning products. The lack of practitioner support for documentation of change in clinical behavior may suggest that they favor traditional attendance- or completion-based CME; this finding requires further research.
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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 | 0.120 | 0.292 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".