Deliberate practice and mastery learning in resuscitation education: A scoping review
Bibliographic record
Abstract
STUDY AIM: To summarize the current state of knowledge of deliberate practice and mastery learning (DP and/or ML) as teaching methods for resuscitation education. METHODS: A scoping review of PubMed, Scopus, and Embase was conducted through March 1, 2021. Studies examining the effect of the incorporation of either deliberate practice and/or mastery learning during resuscitation education were eligible for inclusion. Included studies were dichotomized into studies comparing deliberate practice and/or mastery learning to other training methods (randomized controlled trials) and studies examining before and after impact of deliberate practice and/or mastery learning alone (observational studies). Studies and findings were tabulated and summarized using the scoping review methodology published by Arksey and O'Malley. RESULTS: 63 published studies were screened; sixteen studies met all inclusion criteria (4 randomized controlled trials and 12 observational studies). One randomized controlled trial and eleven observational studies demonstrated improvement in skill and/or knowledge following educational interventions using deliberate practice and/or mastery learning. Significant variability between studies with regard to research designs, learner groups, comparators, and outcomes of interest made quantitative summarization of findings difficult. CONCLUSIONS: The incorporation of deliberate practice and/or mastery learning in resuscitation education may be associated with improved educational outcomes and less skill decay than other educational methods. Current literature on DP and ML suffers from a lack of consistency in research methodology, subjects, and outcomes. Future research should employ uniform definitions for deliberate practice and mastery learning, follow research design that isolates its effect, and examine generalizable and translatable outcomes.
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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 | 0.031 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".