A ‘career exploration’ didactic and simulation-based session increases student knowledge in and exposure to urology
Bibliographic record
Abstract
INTRODUCTION: Though urology attracts well-qualified applicants, students are not typically provided exposure to this smaller specialty until later in their medical education. While simulation-based training continues to supplement medical education, there is a lack of programming to teach specialty-specific procedural skills to medical students and those outside the specialty. We report a half-day simulation and didactic-based approach to increase exposure to urology to interested second-year medical students. METHODS: A half-day didactic- and simulation-based session was offered to second-year medical students (N=57). After a didactic-based overview of the specialty performed by urology providers and a surgical educator, the students participated in small-group simulations, including hands-on simulations. The students completed a post-curriculum survey measuring knowledge gains and soliciting feedback on the session. RESULTS: Students were 57.1% Caucasian, 66.7% female, with a mean age of 24.2 years; 80% stated they were potentially interested in pursuing a surgical specialty such as urology prior to the start of the session. Students reported pre- to post-curriculum gains in knowledge (mean=37%) about a career in urology and basic urologic procedures (p<0.001). Participants were also likely to recommend the curriculum to their peers (p<0.001). CONCLUSIONS: Given that exposure to urology in medical school is usually limited and offered later in training, a half-day didactic- and simulation-based experience for second-year students provides an early introduction and experience within the specialty and its common bedside procedures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".