Evidence‐based medicine training and implementation in surgery: the role of surgical cultures
Bibliographic record
Abstract
PURPOSE: This qualitative study identifies cultural factors that influence the effective implementation of evidence-based medicine (EBM) in surgical practice among Australian surgeons. METHODS: In-depth interviews (n = 22) were conducted with surgeons from a variety of specialties within a large hospital system in Victoria, Australia. The interviews explored the surgeons' understanding of EBM; and challenges to the adoption of EBM. The canons and procedures of the Miles and Huberman's Matrix Analyses approach to qualitative research guided the coding and organization of the data derived from the semi-structured interviews. RESULTS: Surgeons had a good understanding of EBM, but viewed it as little more than a system of evidence, which was often divorced from actual clinical practice. The data also suggested that surgical culture(s) and typologies of surgical style were important variables in the implementation of EBM. The results suggest that the ideal method of EBM implementation is workplace instruction led by surgeons, who exhibit scientist and/or clinician styles of surgical practice; EBM training should occur early in the surgeons' careers; and EBM practice should be role modelled in the presence of trainees by surgeons who exhibit either a scientist and/or clinician style of surgical practice. CONCLUSIONS: The study findings suggest that using pre-existing surgical culture(s) and styles is an important component in the implementation of EBM in surgery. The effective use of the scientist and/or clinician surgeon within the apprenticeship model and the context-specific collegial networks of the surgical profession appear to be key elements in ensuring the successful implementation of EBM in 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 | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".