Being a surgeon or doing surgery? A qualitative study of learning in the operating room
Bibliographic record
Abstract
CONTEXT: A key concern for surgical educators is to prepare students to perform in the operating room while ensuring patient safety. Recent years have seen a renewed discussion of medical education through practice theoretical and sociomaterial lenses. These lenses are introduced to understand and prepare the learner to perform in the given context. This paper takes its point of departure from practice theory by introducing a lens through which to understand learning environments in surgery. METHODS: Using a multi-site ethnographic and practice-based design, this study investigates how aspiring surgical students are stirred into surgical practices and learn to engage as surgeons. During 70 hours of observations of medical students' participation in the operating room, we analysed how the phenomenon of surgical learning can be perceived as instances of transformation in and among social practices. RESULTS: By applying an analytical perspective, this article highlights the use of practice theory in surgical education, which can help to establish a firmer understanding of the learning environment and thereby help educators to improve curricula and prepare students more effectively to enter surgical training. CONCLUSIONS: The use of a practice theory adds the perspective that the education of surgeons needs to take the sayings, doings and relatings that constitute a surgical practice into account when preparing students to perform in their future workplace. In this way, surgical training can be perceived as a process of being stirred into practice. This means that one learns by participating in the practice of providing high-quality care, where the aim is to teach students to be surgeons instead of teaching them to perform surgery.
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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 | 0.021 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".