The Nightmares Course: A Longitudinal, Multidisciplinary, Simulation-Based Curriculum to Train and Assess Resident Competence in Resuscitation
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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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".