A novel simulation competition format as an effective instructional tool in post-graduate medical education
Bibliographic record
Abstract
OBJECTIVE: Medical simulation competitions are a growing reality. This study aims at exploring if a novel format of simulation competition (SIMCUP) can be an effective educational format in post-graduate education. DESIGN: We designed a 2-day event that included scientific educational lectures, an orientation to the competition, familiarization with the simulation lab, and competition time. Day 1 was devoted to preliminary rounds and was structured using an Objective Structured Clinical Examination (OSCE)-like system. On day 2, the first four teams advanced to semi-finals and then to finals, which were held using a classical SimWars style. SETTING AND SUBJECTS: A total of 14 four-participant teams participated in the event over two editions (Ed.1 in 2015 and Ed.2 in 2016). INTERVENTIONS: External referees evaluated both technical and non-technical skills for each simulated scenario. Each participant was also administered pre- and post-test questionnaires covering self-perception about the confidence in managing simulated clinical cases, educational effectiveness, satisfaction with the simulation experience, and previous simulation training. MAIN RESULTS: Overall participants found SIMCUP a useful learning experience, rating it 10 [9, 10] and 10 [7.75-10] out of 10 for Ed.1 and Ed.2, respectively. Participants reported, using a 10-point semantic differential scale ranging from "1 - strongly disagree." to "10 - strongly agree," finding both days to be educationally effective: day 1 was rated 9 [7-10] and 9 [8-10] as day 2 was rated 8 [7-10] and 8 [7-10] for Ed. 1 and Ed. 2, respectively.Participants' self-perception regarding the confidence of managing the specific scenarios significantly improved immediately after the event as measured by pre- and post-questionnaires for all stations and during both editions. CONCLUSION: This study suggests that simulation competition can serve as an effective instructional format in residency training.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.007 | 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".