Does E-learning Facilitate Medical Education in Pediatric Neurology?
Bibliographic record
Abstract
ABSTRACT: Background: E-learning has become commonplace in medical education. Incorporation of multimedia, clinical cases, and interactive elements has increased its attractiveness over textbooks. Although there has been an expansion of e-learning in medicine, the feasibility of e-learning in pediatric neurology is unclear. This study evaluates knowledge acquisition and satisfaction using pediatric neurology e-learning compared to conventional learning. Methods: Residents of Canadian pediatrics, neurology, and pediatric neurology programs and medical students from Queens University, Western University, and the University of Ottawa were invited to participate. Learners were randomly assigned two review papers and two ebrain modules in a four-topic crossover design. Participants completed pre-tests, experience surveys, and post-tests. We calculated the median change in score from pre-test to post-test and constructed a mixed-effects model to determine the effect of variables on post-test scores. Results: In total, 119 individuals participated (53 medical students; 66 residents). Ebrain had a larger positive change than review papers in post-test score from pre-test score for the pediatric stroke learning topic but a smaller positive change for Duchenne muscular dystrophy, childhood absence epilepsy, and acute disseminated encephalomyelitis. Learning topics showed statistical relationship to post-test scores (p = 0.04). Depending on topic, 57–92% (N = 59–66) of respondents favored e-learning over review article learning. Conclusions: Ebrain users scored higher on post-tests than review paper users. However, the effect is small and it is unclear if it is educationally meaningful. Although the difference in scores may not be substantially different, most learners preferred e-learning. Future projects should focus on improving the quality and efficacy of e-learning modules.
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".