The Implementation of a Multi-institutional Multidisciplinary Simulation-based Resuscitation Skills Training Curriculum
Bibliographic record
Abstract
Competency-based curricula require the development of novel simulation-based programs focused on the assessment of entrustable professional activities. The design and delivery of simulation-based programs are labor-intensive and expensive. Furthermore, they are often developed by individual programs and are rarely shared between institutions, resulting in duplicate efforts and the inefficient use of resources. The purpose of this study is to demonstrate the feasibility of implementing a previously developed simulation-based curriculum at a second institution. We sought to demonstrate comparable program-level outcomes between our two study sites. A multi-disciplinary, simulation-based, resuscitation skills training curriculum developed at Queen’s University was implemented at the University of Saskatchewan. Standardized simulation cases, assessment tools, and program evaluation instruments were used at both institutions. Across both sites, 87 first-year postgraduate medical trainees from 14 different residency programs participated in the course and the related research. A total of 226 simulated cases were completed in over 80 sessions. Program evaluation data demonstrated that the instructor experience and learner experience were consistent between sites. The average confidence score (on a 5-point scale) across sites for resuscitating acutely ill patients was 3.14 before the course and 4.23 (p < 0.001) after the course. We have described the successful implementation of a previously developed simulation-based resuscitation curriculum at a second institution. With the growing need for competency-based instructional methods and assessment tools, we believe that programs will benefit from standardizing and sharing simulation resources rather than developing curricula de novo.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".