Testing Test-Enhanced Continuing Medical Education: A Randomized Controlled Trial
Bibliographic record
Abstract
PURPOSE: The authors investigated the impact of the use of an efficient multiple-choice question (MCQ) test-enhanced learning (TEL) intervention for continuing professional development (CPD) on knowledge retention as well as self-reported learning behaviors. METHOD: The authors conducted a randomized controlled trial comparing knowledge retention among learners who registered for an annual CPD conference at the University of Toronto in April 2016. Participants were randomized to receive an online preworkshop stand-alone MCQ test (no feedback) and a postworkshop MCQ test (with feedback) after a 14-day delay. Controls received no pre-/posttesting. The primary outcome measure was performance on a clinical vignette-based retention and application test delivered to all participants four weeks post conference. Secondary outcomes included self-reported changes in learning behavior, satisfaction, and efficiency of TEL. RESULTS: Three hundred eight physicians from across Canada registered for the four-day conference; 186 physicians consented to participate in the study and were randomized to receive TEL or to the control group in 1 of 15 workshops, with 126 providing complete data. A random-effects meta-analysis demonstrated a pooled effect size indicating moderate effect of TEL (Hedges g of 0.46; 95% CI: 0.26-0.67). The majority of respondents (65%) reported improved CPD learning resulting from pretesting. CONCLUSIONS: Testing for learning can be leveraged to efficiently and effectively improve outcomes for CPD. Testing remains an underused education intervention in CPD, and the use of formative assessment to enhance professional development should be a key target for 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".