Protocol for assessing the determinants of preoperative test-ordering behaviour for low-risk surgical procedures using a theoretically driven, qualitative design
Bibliographic record
Abstract
INTRODUCTION: Current evidence suggests that preoperative tests such as chest X-rays, electrocardiograms and baseline laboratory studies may not be useful for healthy patients undergoing low-risk surgical procedures. Routine preoperative testing for healthy patients having low-risk surgery is not a scientifically sound practice. In this study, we will interview healthcare providers working at medical facilities where low-risk surgical procedures are carried out. This will allow us to gain insight into the determinants of preoperative testing behaviours for healthy patients undergoing low-risk surgeries and their barriers and enablers to guideline adherence. METHODS AND ANALYSIS: We will use semistructured interviews with anaesthesiologists, surgeons and preadmission clinic nurses to assess the determinants of preoperative testing behaviours. The interview guide was designed around the Theoretical Domains Framework (TDF), developed specifically to determine the barriers and enablers to implementing evidence-based guidelines. Interviews will be audio-recorded, transcribed verbatim and coded according to the TDF. Key themes will be generated for each of the identified domains. ETHICS AND DISSEMINATION: We have received ethics approval from the Health Research Ethics Board in Newfoundland and Labrador (HREB #2018.190) for this study. The results of this work will be disseminated through a peer-reviewed publication, presentation at a healthcare forum and plain-language infographic summaries. Additionally, deidentified data collected and analysed for this study will be available for review from the corresponding author on reasonable request.
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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 | 0.115 | 0.110 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.097 | 0.019 |
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".