Consensus on Quality Indicators of Postgraduate Medical E-Learning: Delphi Study
Bibliographic record
Abstract
BACKGROUND: The progressive use of e-learning in postgraduate medical education calls for useful quality indicators. Many evaluation tools exist. However, these are diversely used and their empirical foundation is often lacking. OBJECTIVE: We aimed to identify an empirically founded set of quality indicators to set the bar for “good enough” e-learning. METHODS: We performed a Delphi procedure with a group of 13 international education experts and 10 experienced users of e-learning. The questionnaire started with 57 items. These items were the result of a previous literature review and focus group study performed with experts and users. Consensus was met when a rate of agreement of more than two-thirds was achieved. RESULTS: In the first round, the participants accepted 37 items of the 57 as important, reached no consensus on 20, and added 15 new items. In the second round, we added the comments from the first round to the items on which there was no consensus and added the 15 new items. After this round, a total of 72 items were addressed and, of these, 37 items were accepted and 34 were rejected due to lack of consensus. CONCLUSIONS: This study produced a list of 37 items that can form the basis of an evaluation tool to evaluate postgraduate medical e-learning. This is, to our knowledge, the first time that quality indicators for postgraduate medical e-learning have been defined and validated. The next step is to create and validate an e-learning evaluation tool from these items.
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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 | 0.003 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".