Barriers to reducing preoperative testing for low-risk surgical procedures: A qualitative assessment guided by the Theoretical Domains Framework
Bibliographic record
Abstract
INTRODUCTION: While numerous guidelines do not recommend preoperative tests for low risk patients undergoing low risk surgeries, they are often routinely performed. Canadian data suggests preoperative tests (e.g. ECGs and chest x-rays) preceded 17.9%-35.5% of low-risk procedures. Translating guidelines into clinical practice can be challenging and it is important to understand what is driving behaviour when developing interventions to change it. AIM: Thus, we completed a theory-based investigation of the perceived barriers and enablers to reducing unnecessary preoperative tests for low-risk surgical procedures in Newfoundland, Canada. METHOD: We used snowball sampling to recruit surgeons, anaesthesiologists, or preoperative clinic nurses. Interviews were conducted by two researchers using an interview guide with 31 questions based on the theoretical domains framework. Data was transcribed and coded into the 14 theoretical domains and then themes were identified for each domain. RESULTS: We interviewed 17 surgeons, anaesthesiologists, or preoperative clinic nurses with 1 to 34 years' experience. Overall, while respondents agreed with the guidelines they described several factors, across seven relevant theoretical domains, that influence whether tests are ordered. The most common included uncertainty about who is responsible for test ordering, inability to access patient records or to consult/communicate with colleagues about ordering decisions and worry about surgery delays/cancellation if tests are not ordered. Other factors included workplace norms that conflicted with guidelines and concerns about missing something serious or litigation. In terms of enablers, respondents believed that clear institutional guidelines including who is responsible for test ordering and information about the risk of missing something serious, supported by improved communication between those involved in the ordering process and periodic evaluation will reduce any unnecessary preoperative testing. CONCLUSION: These findings suggest that both health system and health provider factors need to be addressed in an intervention to reduce pre-operative testing.
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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 | 0.026 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".