Prioritization in medical school simulation curriculum development using survey tools and desirability function: a pilot experiment
Bibliographic record
Abstract
BACKGROUND: In Italy, there is no framework of procedural skills that all medical students should be able to perform autonomously at graduation. The study aims at identifying (1) a set of essential procedural skills and (2) which abilities could be potentially taught with simulation. Desirability score was calculated for each procedure to determine the most effective manner to proceed with simulation curriculum development. METHODS: A web poll was conducted at the School of Medicine in Novara, looking at the level of expected and self-perceived competency for common medical procedures. Three groups were enrolled: (1) faculty, (2) junior doctors in their first years of practice, and (3) recently graduated medical students. Level of importance of procedural skills for independent practice expressed by teachers, level of mastery self-perceived by learners (students and junior doctors) and suitability of simulation training for the given technical skills were measured. Desirability function was used to set priorities for future learning. RESULTS: The overall mean expected level of competency for the procedural skills was 7.9/9. Mean level of self reported competency was 4.7/9 for junior doctors and 4.4/9 for recently graduated students. The highest priority skills according to the desirability function were urinary catheter placement, nasogastric tube insertion, and incision and drainage of superficial abscesses. CONCLUSIONS: This study identifies those technical competencies thought by faculty to be important and assessed the junior doctors and recent graduates level of self-perceived confidence in performing these skills. The study also identifies the perceived utility of teaching these skills by simulation. The study prioritizes those skills that have a gap between expected and observed competency and are also thought to be amenable to teaching by simulation. This allows immediate priorities for simulation curriculum development in the most effective manner. This methodology may be useful to researchers in other centers to prioritize simulation 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 | 0.042 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".