The Effect of Spaced Repetition on Learning and Knowledge Transfer in a Large Cohort of Practicing Physicians
Bibliographic record
Abstract
PURPOSE: Spaced repetition is superior to repeated study for learning and knowledge retention, but literature on the effect of different spaced repetition strategies is lacking. The authors evaluated the effects of different spaced repetition strategies on long-term knowledge retention and transfer. METHOD: This prospective cohort study, conducted from October 1, 2020, to July 20, 2023, used the American Board of Family Medicine Continuous Knowledge Self-Assessment (CKSA) to assess learning and knowledge transfer of diplomates and residents. Participants were randomized to a control group or 1 of 5 spaced repetition conditions during 5 calendar quarters (January 1, 2021, to March 31, 2022). Participants in the spaced repetition groups received 6 repeated questions once or twice. Incorrectly but confidently answered questions were prioritized for repetition, with decreasing priority for questions answered incorrectly with lesser confidence. All participants received 6 rewritten questions corresponding to their initial questions chosen for repetition in quarter 10 (second quarter of calendar year 2023). RESULTS: A total of 26,258 family physicians or residents who completed the CKSA in the baseline period were randomized. Spaced repetition was superior to no spaced repetition for learning at quarter 6 (58.03% vs 43.20%, P < .001, Cohen d = 0.62) and knowledge transfer at quarter 10 (58.33% vs 52.39%, P < .001, Cohen d = 0.26). Double-spaced repetitions were superior to single-spaced repetitions for learning (62.24% vs 51.83%, P < .001, Cohen d = 0.43) and transfer (60.08% vs 55.72%, P < .001, Cohen d = 0.20). There were no meaningful differences in learning or transfer between repetition strategy chosen in the single- or double-repetition groups. CONCLUSIONS: This study affirms the value of spaced repetition in improving learning and retention in medical education and ongoing professional development.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".