Promoting Readiness for Residency: Embedding Simulation-Based Mastery Learning for Breaking Bad News Into the Medicine Subinternship
Bibliographic record
Abstract
PURPOSE: It is challenging to add rigorous, competency-based communication skills training to existing clerkship structures. The authors embedded a simulation-based mastery learning (SBML) curriculum into a medicine subinternship to demonstrate feasibility and determine the impact on the foundational skill of breaking bad news (BBN). METHOD: All fourth-year students enrolled in a medicine subinternship at Northwestern University Feinberg School of Medicine from September 2017 through August 2018 were expected to complete a BBN SBML curriculum. First, students completed a pretest with a standardized patient using a previously developed BBN assessment tool. Learners then participated in a 4-hour BBN skills workshop with didactic instruction, focused feedback, and deliberate practice with simulated patients. Students were required to meet or exceed a predetermined minimum passing standard (MPS) at posttest. The authors compared pretest and posttest scores to evaluate the effect of the intervention. Participant demographic characteristics and course evaluations were also collected. RESULTS: Eighty-five students were eligible for the study, and 79 (93%) completed all components. Although 55/79 (70%) reported having personally delivered serious news to actual patients, baseline performance was poor. Students' overall checklist performance significantly improved from a mean of 65.0% (SD = 16.2%) items correct to 94.2% (SD = 5.9%; P < .001) correct. There was also statistically significant improvement in scaled items assessing quality of communication, and all students achieved the MPS at mastery posttest. All students stated they would recommend the workshop to colleagues. CONCLUSIONS: It is feasible to embed SBML into a required clerkship. In the context of this study, rigorous SBML resulted in uniformly high levels of skill acquisition, documented competency, and was positively received by learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".