A low-fidelity simulation curriculum addresses needs identified by faculty and improves the comfort level of senior internal medicine resident physicians with inhospital resuscitation*
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to describe the essential elements of in hospital resuscitation knowledge and skills for senior internal medicine resident physicians and to evaluate a low-fidelity simulation course that incorporates these elements. DESIGN: In part 1, attending physicians were electronically surveyed using a modified Dillman method. A broad list of knowledge skills sets was gathered from recent resuscitation guidelines. In part 2, a 2-day, low-fidelity simulation, case-based curriculum was designed based on the results of part 1. Course participants were surveyed 1 month before and 1 month after the course. SETTING: Four academic teaching hospitals. PARTICIPANTS: Attending physicians in cardiology, critical care, and internal medicine responded to the needs assessment survey. A convenience sample of internal medicine residents responded to the surveys before and after the course. MEASUREMENTS: Respondents ranked items on a 6-point Likert scale for all surveys. Responses were collated using descriptive statistics. This study met the requirements of the Research Ethics Board. MAIN RESULTS: In part 1, the response rate was 75% (n = 93), with the majority (52%) of respondents being internal medicine attending physicians. The top five knowledge sets were cardiac rhythm assessment, discussion of code status, delivery of bad news, management of wide complex tachycardia, and management of bradycardia. The top five skills were defibrillation, airway assessment, bag-mask ventilation, central venous access, and cardioversion. In part 2, the response rate was 93% (n = 27) before and 85% (n = 23) after course. Only 28% of residents felt prepared to lead resuscitations before the course. After the course, 45% of participants reporting using the knowledge and skills during a resuscitation. Significant changes in median confidence scores before to after the course occurred in important domains. CONCLUSIONS: The results of the needs assessment should be used to tailor resuscitation education for residents. An educational need exists for resident physicians. This low-fidelity simulation course improves self-reported confidence in resuscitation knowledge and skills.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| 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".