Influence of undergraduate medical education exposure to cadaveric dissection on choice of surgical specialty: a national survey of Canadian surgical residents
Bibliographic record
Abstract
Background: The number of Canadian Residency Matching Service (CaRMS) applicants ranking surgical specialties as their first choice has declined over the past 20 years; concurrently, there has been a reduction in the number of hours spent teaching undergraduate medical education (UGME) anatomy, particularly with cadaveric dissection. The aim of this study was to determine the factors that most influence selection of a surgical specialty, with specific focus on the impact of UGME anatomy training. Methods: A 36-item cross-sectional survey was designed by experts in medical education and distributed to all current surgical residents in Canada in October 2018. Responses were recorded on a 5-point Likert scale or by means of list ranking. We analyzed univariable outcomes with a t test for continuous outcomes and the χ2 test for dichotomous outcomes. Results: Of 1493 surgical residents, 228 responded to the survey (response rate 15.3%). Respondents reported experiences on core rotations and elective rotations, and access to a mentor as the most important factors in deciding to pursue a surgical residency. Anatomy training with or without cadaveric dissection was moderately influential in respondents' first-choice CaRMS discipline (mean Likert scale score 2.97 [standard deviation (SD) 1.34] and 2.87 [SD 1.26], respectively). General surgery residents' CaRMS applications were more likely to have been influenced by UGME anatomy training than the applications by residents in other surgical specialties (p < 0.001). The impact of UGME anatomy training did not vary between postgraduate years or between male and female residents. Conclusion: Canadian surgical residents' decision to apply to a surgical specialty did not seem to be strongly influenced by their UGME anatomy training, with or without cadaveric dissection, but, rather, by factors such as clinical experience and surgical mentorship. Further evaluation of groups that were more positively affected by their UGME anatomy training is warranted.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".