52. Simulation based training improves resident competence in the performance of critical resuscitation procedures
Bibliographic record
Abstract
We sought to assess the impact of procedural skills simulation training on residents’ competence in performing critical resuscitation skills. Our study was a prospective, cross-sectional study of residents from three residency training programs (Family Medicine, Emergency Medicine and Internal Medicine) at the University of Calgary. Participants completed a survey measuring competence in the performance of the procedural skills required to manage hemodynamic instability. The study intervention was an 8 hour simulation based training program focused on resuscitation procedure psychomotor skill acquisition. Competence was criterion validated at the Right Internal Jugular Central Venous Catheter Insertion station by an expert observer using a standardized checklist (Observed Structured Clinical Examination (OSCE) format). At the completion of the simulation course participants repeated the self-assessment survey. Descriptive Statistics, Cronbach’s alpha, Pearson’s correlation coefficient and Paired Sample t-test statistical tools were applied to the analyze the data. Thirty-five of 37 residents (9 FRCPC Emergency Medicine, 4 CCFP-Emergency Medicine, 17 CCFP, and 5 Internal Medicine) completed both survey instruments and the eight hour course. Seventy-two percent of participants were PGY-1 or 2. Mean age was 30.7 years of age. Cronbach’s alpha for the survey instrument was 0.944. Pearson’s Correlation Coefficient was 0.69 (p < 0.001) for relationship between Expert Assessment and Self-Assessment. The mean improvement in competence score pre- to post-intervention was 6.77 (p < 0.01, 95% CI 5.23-8.32). Residents from a variety of training programs (Internal Medicine, Emergency Medicine and Family Medicine) demonstrated a statistically significant improvement in competence with critical resuscitation procedural skills following an intensive simulation based training program. Self-assessment of competence was validated using correlation data based on expert assessments. Dawson S. Procedural simulation: a primer. J Vasc Interv Radiol. 2006; 17(2.1):205-13. Vozenilek J, Huff JS, Reznek M, Gordon JA. See one, do one, teach one: advanced technology in medical education. Acad Emerg Med. 2004; 11(11):1149-54. Ziv A, Wolpe PR, Small SD, Glick S. Simulation-based medical education: an ethical imperative. Acad Med. 2003; 78(8):783-8.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".