Preparing to Operate: A Multinational Analysis of Practices and Perceptions of Surgical Residents
Bibliographic record
Abstract
INTRODUCTION: Operative preparation among surgical residents is a critical aspect of surgical training, requiring a balance of technical and non-technical skill development. Structured residency programmes have introduced both opportunities and challenges for residents, including navigating diverse educational resources and addressing barriers to effective preparation. This study aimed to examine operative preparation among surgical residents, focusing on their attitudes, motivations, barriers and strategies to assess the efficacy of residency programmes. METHODS: A cross-sectional survey, designed by surgeons and surgical residents, gathered data from 201 surgical residents across various specialties and countries. The survey investigated demographics, practices, influences and experiences regarding residency programmes. RESULTS: The study revealed a positive correlation between time spent preparing and perceived preparedness, with residents spending more time preparing as they progressed in their residency. Barriers to preparation included limited time and energy as well as having a minimal role in the surgery. Motivations were centred around personal growth, as well as safety and complication avoidance. Common preparation techniques included reviewing imaging (88.6%), watching surgical videos (83.6%) and reviewing medical records (82.1%). Activities such as reviewing articles and mental rehearsal were utilised less but were strongly correlated with preparedness. Only 31% received formal training on preoperative preparation. CONCLUSIONS: Surgical residency programmes need to address the diverse learning preferences of residents and provide a more structured approach to preparation. There is a need to optimise barriers and motivators to preoperative preparation and align expectations between tutors and residents to enhance the preparedness of surgical residents for the operating room.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".