A novel simulation competition format as an effective instructional tool in post-graduate medical education
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it