Status of simulation in health care education: an international survey
Bibliographic record
Abstract
Karim Qayumi,1 George Pachev,2 Bin Zheng,3 Amitai Ziv,4 Valentyna Koval,1 Sadia Badiei,5 Adam Cheng6 1Center of Excellence for Simulation Education and Innovation, Department of Surgery, 2Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada; 3Surgical Simulation Research Laboratory, Department of Surgery, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB, Canada; 4Israel Center for Medical Simulation, Chaim Sheba Medical Center and Sackler Medical School, Tel Aviv University, Tel Aviv, Israel; 5Centre of Excellence for Simulation Education and Innovation, University of British Columbia, Vancouver, BC, Canada; 6KidSIM-ASPIRE Simulation Research Program, Alberta Children’s Hospital, University of Calgary, Calgary, AB, CanadaAbstract: Simulation is rapidly penetrating the terrain of health care education and has gained growing acceptance as an educational method and patient safety tool. Despite this, the state of simulation in health care education has not yet been evaluated on a global scale. In this project, we studied the global status of simulation in health care education by determining the degree of financial support, infrastructure, manpower, information technology capabilities, engagement of groups of learners, and research and scholarly activities, as well as the barriers, strengths, opportunities for growth, and other aspects of simulation in health care education. We utilized a two-stage process, including an online survey and a site visit that included interviews and debriefings. Forty-two simulation centers worldwide participated in this study, the results of which show that despite enormous interest and enthusiasm in the health care community, use of simulation in health care education is limited to specific areas and is not a budgeted item in many institutions. Absence of a sustainable business model, as well as sufficient financial support in terms of budget, infrastructure, manpower, research, and scholarly activities, slows down the movement of simulation. Specific recommendations are made based on current findings to support simulation in the next developmental stages. Keywords: simulation, health care education, innovation, medical, simulation center, technology-enabled learning
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".