Systematic review of distributed practice and retrieval practice in health professions education
Bibliographic record
Abstract
To determine the effect of distributed practice (spacing out of study over time) and retrieval practice (recalling information from memory) on academic grades in health professions education and to summarise a range of interventional variables that may affect study outcomes. A systematic search of seven databases in November 2022 which were screened according to predefined inclusion criteria. The Medical Education Research Study Quality Instrument (MERSQI) and Newcastle-Ottawa Scale-Education (NOS-E) were used to critically appraise eligible articles. A summary of interventional variables includes article content type, strategy type, assessment type and delay and statistical significance. Of 1818 records retrieved, 56 were eligible for inclusion and included a total of 63 experiments. Of these studies, 43 demonstrated significant benefits of distributed practice and/or retrieval practice over control and comparison groups. Included studies averaged 12.23 out of 18 on the MERSQI and averaged 4.55 out of 6 on the NOS-E. Study designs were heterogeneous with a variety of interventions, comparison groups and assessment types. Distributed practice and retrieval practice are effective at improving academic grades in health professions education. Future study quality can be improved by validating the assessment instruments, to demonstrate the reliability of outcome measures. Increasing the number of institutions included in future studies may improve the diversity of represented study participants and may enhance study quality. Future studies should consider measuring and reporting time on task which may clarify the effectiveness of distributed practice and retrieval practice. The stakes of the assessments, which may affect student motivation and therefore outcomes, should also be considered.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".