A needs assessment study of undergraduate surgical education.
Bibliographic record
Abstract
BACKGROUND: There is compelling evidence to suggest that undergraduate surgical education may fail to provide appropriate instruction in basic surgical principles and skills. METHODS: We completed a descriptive, cross-sectional survey of stakeholder groups (surgeon educators and recent medical school graduates) to assess the perceived relevance and learning for surgical principles, surgical skills, teaching environments and teaching interventions. RESULTS: Graduates returned 123 surveys, and surgeons returned 55 surveys (response rates: graduates 46%, surgeons 45%). Both graduates and surgeons considered 8 of 10 surgical principles highly relevant to current medical practice. Despite this, the surgical clerkship seemed to enable proficiency in far fewer principles (graduates: 3, surgeons: 5). Graduates believed that each of the 15 basic surgical skills is relevant to current medical practice, whereas surgeons indicated that more invasive skills (i.e., central venous lines, thoracentesis) are much less relevant. Graduates and surgeons indicated that medical students will achieve proficiency in only 3 basic skills areas as a result of the surgical clerkship. Graduates and surgeons considered each surgical specialty relevant and effective in undergraduate surgical education. According to graduates and surgeons, the most effective teaching environments are outpatient settings (emergency department, outpatient clinics). Graduates and surgeons ranked resident teaching as the most effective teaching intervention, and traditional interventions (grand rounds, formal rounds) and electronic resources (computer-assisted learning, web-based learning) were ranked the least effective. CONCLUSIONS: In this study, we assessed the learning needs of contemporary medical students in surgery. The results suggest that respondent graduate students and surgeons believe that the level of proficiency achieved in surgical principles and basic skills through undergraduate surgical educations is much less than anticipated. Outpatient settings and resident teaching are believed to provide the most effective teaching for medical students. Information from this study has important implications for Canadian undergraduate surgery programs and curricula.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".