Understanding How Surgeons Improve the Quality of Breast Cancer Surgery Using the Theoretical Domains Framework
Bibliographic record
Abstract
Objective: To understand how surgeons improve the quality of breast cancer surgery. Background: Between 2007 and 2021, breast cancer surgeons in Manitoba, Canada, participated in national initiatives to build a local capacity for quality improvement (QI) in cancer surgery. Key aspects of these initiatives include audit and feedback reports using data from synoptic operative reports and communities of practice. Surgeon engagement in breast cancer surgery QI in Manitoba has not been evaluated since the initiatives were concluded in 2021. Methods: We conducted 60-minute virtual semi-structured qualitative interviews with surgeons who performed breast cancer surgery in Manitoba, Canada, between 2021 and 2024. The interviews were guided by the theoretical domain framework. The thematic analyses were performed by 2 independent researchers. Results: Twelve surgeons were interviewed. Surgeons were motivated to ensure timely care close to home, with excellent oncological, surgical, and aesthetic outcomes. They felt capable of monitoring and improving their surgical quality by tracking their own metrics, collaborating with multidisciplinary colleagues, engaging in continuous professional development, and advocating for improvement. Audit and feedback reports were not perceived to improve the quality of surgery. They felt limited opportunities to sustain improvement strategies. Resource constraints and leadership support within the healthcare system were major barriers to achieving their ideal quality of care. Conclusion: Surgeons performing breast cancer surgery in Manitoba were motivated and capable of improving the quality of breast cancer surgery. However, they perceive limited opportunities and barriers within the healthcare systems to doing so. Future research will provide information on broader contextual factors affecting breast cancer surgery QI.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".