Perioperative simulation-based medical education in China: a nationwide cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Simulation-based medical education (SBME) has rapidly emerged as a new trend in perioperative medical education in China. However, our understanding of the role and impact of SBME on medical education remains limited. This study surveyed medical educators, including administrators, instructors, and learners, to evaluate perioperative SBME by identifying its advantages and disadvantages. METHODS: A cross-sectional online survey was conducted in 2024, involving administrators, instructors, and learners from medical education institutes across China. The survey included a structured questionnaire focused on perioperative SBME with information regarding participant characteristics, their attitudes, and perceived teaching obstacles. Descriptive statistics were used to summarize the current status of perioperative SBME, and logistic regression analysis helped identify factors potentially affecting learners' satisfaction with the program. RESULTS: A total of 1411 survey respondents, including 274 administrative staff, 285 course instructors, 681 learners, and 250 others, participated in our study. Among administrators, 81.8% believed that SBME was crucial for perioperative medical training, emphasizing its role in enhancing trainees' clinical skills and decision-making abilities. Key challenges identified by administrators and instructors included insufficient training space, simulation models, and faculty. Administrators advocated for increased investment in simulation resources (88.7%) and faculty development (84.3%). Instructors stressed the importance of enhanced faculty training (92.6%), implementing standardized training protocols (69.1%), and increasing the proportion of simulation-based teaching (68.1%). Learners recommended optimizing teaching schedules (65.9%) and improving simulation methods (64.2%). Notably, SBME implementation varied across different regions in China, especially in tertiary hospitals, where medical institutes in Southern and Eastern China run more SBME programs. CONCLUSIONS: Perioperative SBME development has progressed in China, but faces imbalances across different institutions. Furthermore, the availability of qualified instructors, equipment, and financial investment still need to be addressed. This study provides a foundation for the continued advancement of SBME. However, as participation was voluntary and response rates could not be calculated, results may not be fully generalizable. These findings nonetheless provide an evidence base for national strategies to standardize and expand simulation-based perioperative education in China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".