The Impact of Early Medical School Surgical Exposure on Interest in Neurosurgery
Bibliographic record
Abstract
BACKGROUND: Medical student interest in neurosurgery is decreasing and resident attrition is trending upwards in favor of more lifestyle-friendly specialties that receive greater exposure during medical school. The University of Toronto began offering an annual two week comprehensive, focused surgical experience (Surgical Exploration and Discovery (SEAD) program) to 20 first year medical students increasing exposure to surgical careers. This study determines how SEAD affects students' views of a career in neurosurgery. METHODS: Surveys were administered to 38 SEAD participants over two program cycles. Information was obtained regarding demographics, impacts of SEAD, and factors affecting career decision making. Subgroup analyses assessed for factors predicting pre- and post-intervention interest in neurosurgery. RESULTS: Ninety-seven percent (n=37) of students completed the survey. Before SEAD, 25% were interested in neurosurgery but this decreased to 10% post-SEAD (p=0.001). However, post-SEAD interest increased from 10% to 38% if lifestyle factors were theoretically controlled across surgical specialties (p<0.005). A majority (81%) felt SEAD improved their understanding of neurosurgery, 62.2% felt that exposure to other surgical specialties reduced their interest in neurosurgery, and 21% felt SEAD increased their interest in neurosurgery. Nineteen percent intended to explore neurosurgery further with observerships and one student planned to organize neurosurgical research. CONCLUSIONS: This surgical exposure intervention increased understanding about neurosurgery and reduced overall interest in neurosurgery as a career. However, those remaining interested were motivated to plan further neurosurgical clinical experiences. The SEAD program may, therefore, aid in early selection of students motivated to satisfy the demands of a neurosurgical career.
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How this classification was reachedexpand
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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".