Early Exposure to Neurosurgery: Assessment of Perceptions, Mentorship and Competence on Medical Student Interest
Bibliographic record
Abstract
ABSTRACT Background: Neurosurgery is a demanding specialty, and a trainee’s exposure to its tenets is usually achieved through residency. Medical students only access neurosurgical knowledge via brief stints in clerkships/electives and often lack mentorship and early exposure. This study sought to investigate the varying expectations about neurosurgical training held by Canadian medical students, with the goal of determining the impact of early exposure through educational opportunities and mentorship in developing interest and familiarity in the field. Methods: A cross-sectional study across Canada was conducted where students were provided with a 35-point questionnaire pertaining to mentorship, educational opportunities and interests regarding neurosurgery through REDcap. Questions were open-ended, closed-ended (single choice) or five-point Likert scale (matrix format). Interest in pursuing neurosurgery was selected as the primary outcome of this study and was dichotomized into high or low interest. Predictors of interest were determined using multivariable logistic regressions. Results: A total of 136 students from 14 accredited Canadian medical schools responded to the study. Most (55.9%) had prior exposure, and the most commonly reported deterring factors were work–life balance (94.5%) and family (84.6%). Predictors of interest included participation in relevant case-based discussion (OR = 2.644, 95% CI [1.221–5.847], p = 0.015) and involvement in neurosurgical research encouraged by home institution (OR = 1.619, 95% CI [1.124–2.396], p = 0.012). Discussion Future efforts to improve student interest should focus on early exposure to the field such as developing pre-clerkship neurosurgical electives or medical student groups focused on neurosurgery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".