Developing Surgical Antimicrobial Prophylaxis Interventions Using Theoretical Domains Framework
Bibliographic record
Abstract
Surgical site infections are common causes of healthcare-associated infections. Using surgical antimicrobial prophylaxis (SAP) is a complex process that can reduce these rates if performed correctly. While antimicrobial stewardship programs have developed guidelines for SAP, there has been less focus on understanding and modifying the behavioral and contextual factors required to optimize prophylaxis use. We performed chart reviews and workflow analyses to develop interventions based on a theoretical framework to improve SAP use in two academic hospitals. SAP use during a one month period (October 2016) was analyzed for orthopedic and general surgery procedures by chart review. The primary outcomes of interest included SAP choice, preoperative timing, intraoperative re-dosing, and postoperative continuation. Structured workflow analyses were performed to understand the processes involved in SAP ordering and administration. These findings were applied to the Theoretical Domains Framework (TDF) to develop theory-based interventions. We reviewed 88 orthopedic and 63 general surgery procedures. Adherence to institutional guidelines for prophylaxis choice was low in both orthopedic (55%) and general surgery (70%). For general surgery, preoperative timing was incorrect in 25% of cases, re-dosing for procedure duration was incorrect in 59% of cases, and re-dosing for blood loss was not routinely performed. Alternatively, for orthopedic surgery cefazolin was re-dosed too early, at a median of 93 minutes (n = 42), and postop antibiotic use was continued for 10 days in all 14 aseptic hip revisions. There was variation in practice patterns among orthopedic surgeons. Using TDF, we identified barriers among numerous theoretical domains for re-dosing (knowledge; memory, attention, and decision processes; environmental context and resources), choice of antibiotic and postoperative duration (knowledge; beliefs about consequences; emotion; social influences). We identified suboptimal SAP use in two surgical services, each with distinct deficiencies. Performing in-depth chart reviews and workflow analyses characterized the specific behavioural and contextual barriers that require intervention. All authors: No reported disclosures.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".