The Mentee Perspective: Evaluating Mentorship of Medical Students in the Field of Orthopaedic Surgery
Bibliographic record
Abstract
INTRODUCTION: Mentorship is an invaluable facet of medical education. The purpose of this study was to analyze medical student perspectives of mentorship they received and the influence this has on their participation in the field of orthopaedic surgery. METHODS: We conducted a cross-sectional study of medical students interested in pursuing orthopaedic surgery through an 18-question survey distributed through social media and e-mail. RESULTS: Two hundred fifteen students completed this survey, with over 50% of students reporting that they have a mentor in orthopaedic surgery while 34% were actively seeking one. Most students found mentors through research opportunities (25%) and cold e-mails (20%). Common hurdles to mentorship were access (38%) and finding common time (30%). Peer mentorship had a higher mean satisfaction score in all domains, except facilitating matching, and there was a significant difference between groups (e.g., peer mentor versus program director; P < 0.001). Sex, race, and degree type were not significantly related to students' access to or their evaluation of mentors (P > 0.05 for all). CONCLUSION: Overall, this study demonstrates that medical students across the nation rely on mentorship to guide them on their path to becoming an orthopaedic surgeon.
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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 | 0.013 | 0.034 |
| 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 | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".