Before the scalpel: Exploring surgical residents' preoperative preparatory strategies
Bibliographic record
Abstract
OBJECTIVE: This study sought to increase understanding of preoperative preparatory strategies utilised by senior surgical residents and identify how social and material forces come together to shape practice. SUMMARY/BACKGROUND DATA: Preoperative preparation can play a powerful role in operative learning. Residents rarely receive guidance, feedback, or explicit expectations on how to prepare for the OR. Understanding current practice and how to support preoperative preparation represents an important gap in our efforts to improve surgical training. METHODS: Constructivist grounded theory with sensitizing concepts from sociomateriality guided data collection and analysis. Fifteen senior surgical residents from a range of surgical disciplines were purposefully sampled and participated in an in-depth individual interview. Two return-of-finding focus groups followed with seven residents. Rigor was enhanced through constant comparison, theoretical sampling, pursuit of discrepant data, and investigator triangulation. RESULTS: Residents utilised a range of strategies addressing four areas of focus: develop technical skills, improve procedural knowledge, enhance patient-specificity, and know surgical preferences. However, residents also described receiving limited guidance on what it means to 'be prepared' and experience significant challenges in achieving preparedness. A mix of social and material things that enabled or constrained preparatory efforts influenced individual strategies. These included rotation structure, relationships, the OR list, and time. CONCLUSIONS: Our findings offer possible solutions by elaborating on preparatory variability and considerations for residents, faculty, and programs to improve practice. As a first step, we suggest programs begin to engage in explicit dialogue and reflection with their residents, faculty, and residency program committees.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".