Rapid Cycle Deliberate Practice in Medical Education - a Systematic Review
Bibliographic record
Abstract
Rapid Cycle Deliberate Practice (RCDP) is a novel simulation-based education model that is currently attracting interest, implementation, exploration and research in medical education. In RCDP, learners rapidly cycle between deliberate practice and directed feedback within the simulation scenario until mastery is achieved. The objective of this systematic review is to examine the literature and summarize the existing knowledge on RCDP in simulation-based medical education. Fifteen resources met inclusion criteria; they were diverse and heterogeneous, such that we did not perform a quantitative synthesis or meta-analysis but rather a narrative review on RCDP. All resources described RCDP in a similar manner. Common RCDP implementation strategies included: splitting simulation cases into segments, micro debriefing in the form of 'pause, debrief, rewind and try again' and providing progressively more challenging scenarios. Variable outcome measures were used by the studies including qualitative assessments, scoring tools, procedural assessment using checklists or video review, time to active skills and clinical reports. Results were limited and inconsistent. There is an absence of data on retention after RCDP teaching, on RCDP, with learners from specialties other than pediatrics, on RCDP for adult resuscitation scenarios and if RCDP teaching translates into practice change in the clinical realm. We have identified important avenues for future research on RCDP.
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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 | 0.017 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".