Does testing enhance learning in continuing medical education?
Bibliographic record
Abstract
Background: There has been growing interest in using theory-driven research to develop and evaluate continuing medical education (CME) activities. Within health professions education, testing has been shown to promote learning in a variety of different contexts, an effect referred to as test-enhanced learning (TEL). However, the extent to which TEL generalizes to CME remains unclear. The purpose of this study was to investigate whether physicians who received two intervening tests following a CME event would experience a TEL effect relative to physicians who received additional study material to review without testing. Methods: Forty-nine physicians were recruited during a local CME activity. Physicians were randomized to either a) the test group (n=26), where participants completed two 20 multiple-choice question (MCQ) quizzes related to the lecture content or b) the study group (n=23), where participants studied the same information without testing. Testing and studying occurred independently during the CME activity, and then four weeks later online. At eight weeks, participants completed a final 20-item MCQ online test. A between-subjects t-test was used to compare performance on the final test as a function of the initial educational activity (test group vs. study group).Results: Performance on the final MCQ test was equivalent for both test (Mean (SD): 75% (9.9)) and study-only (77% (7.3)) conditions (t(47) = 0.94, p=0.35). Conclusion: The null findings in the present study are contrary to previous findings demonstrating TEL among novice learner populations. The lack of TEL highlights several programmatic considerations that should be factored in before implementing TEL as a part of CME.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.006 | 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".