The Nightmares Course: A Longitudinal, Multidisciplinary, Simulation-Based Curriculum to Train and Assess Resident Competence in Resuscitation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Postgraduate medical education programs would benefit from a robust process for training and assessment of competence in resuscitation early in residency. OBJECTIVE: To describe and evaluate the Nightmares Course, a novel, competency-based, transitional curriculum and assessment program in resuscitation medicine at Queen's University in Kingston, Ontario, Canada. METHODS: First-year residents participated in the longitudinal Nightmares Course at Queen's University during the 2015-2016 academic year. An expert working group developed the entrustable professional activity and curricular design for the course. Formative feedback was provided following each simulation-based session, and we employed a summative objective structured clinical examination (OSCE) utilizing a modified Queen's Simulation Assessment Tool. A generalizability study and resident surveys were performed to evaluate the course and assessment process. RESULTS: A total of 40 residents participated in the course, and 23 (58%) participated in the OSCE. Eight of 23 (35%) did not meet the predetermined competency threshold and required remediation. The OSCE demonstrated an acceptable phi coefficient of 0.73. The approximate costs were $240 per Nightmares session, $10,560 for the entire 44-session curriculum, and $3,900 for the summative OSCE. CONCLUSIONS: The Nightmares Course demonstrated feasibility and acceptability, and is applicable to a broad array of postgraduate medical education programs. The entrustment-based assessment detected several residents not meeting a minimum competency threshold, and directed them to additional training.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it