Perfecting practice: a protocol for assessing simulation-based mastery learning and deliberate practice versus self-guided practice for bougie-assisted cricothyroidotomy performance
Bibliographic record
Abstract
BACKGROUND: Simulation-based medical education (SBME) is a cornerstone for procedural skill training in residency education. Multiple studies have concluded that SBME is highly effective, superior to traditional clinical education, and translates to improved patient outcomes. Additionally it is widely accepted that mastery learning, which comprises deliberate practice, is essential for expert level performance for routine skills; however, given that highly structured practice is more time and resource-intensive, it is important to assess its value for the acquisition of rarely performed technical skills. The bougie-assisted cricothyroidotomy (BAC), a rarely performed, lifesaving procedure, is an ideal skill for evaluating the utility of highly structured practice as it is relevant across many acute care specialties and rare - making it unlikely for learners to have had significant previous training or clinical experience. The purpose of this study is to compare a modified mastery learning approach with deliberate practice versus self-guided practice on technical skill performance using a bougie-assisted cricothyroidotomy model. METHODS: A multi-centre, randomized study will be conducted at four Canadian and one American residency programs with 160 residents assigned to either mastery learning and deliberate practice (ML + DP), or self-guided practice for BAC. Skill performance, using a global rating scale, will be assessed before, immediately after practice, and 6 months later. The two groups will be compared to assess whether the type of practice impacts performance and skill retention. DISCUSSION: Mastery learning coupled with deliberate practice provides systematic and focused feedback during skill acquisition. However, it is resource-intensive and its efficacy is not fully defined. This multi-centre study will provide generalizable data about the utility of highly structured practice for technical skill acquisition of a rare, lifesaving procedure within postgraduate medical education. Study findings will guide educators in the selection of an optimal training strategy, addressing both short and long term performance.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".