Clinic barriers and facilitators to the implementation of a decolonization strategy for Staphylococcus aureus in hip and knee arthroplasty: A qualitative study
Bibliographic record
Abstract
Abstract Background Decolonization is an important infection prevention and control strategy in the surgical context. Preoperative decolonization of hip and knee replacement patients reduces the incidence of surgical site infections (SSIs), but the implementation of decolonization protocols has been uneven. Understanding the clinical level barriers and facilitators that affect implementation may increase the effectiveness of preoperative decolonization strategies. Methods Leveraging ongoing quality improvement (QI) activity to reduce SSIs amongst hip and knee replacement patients in Alberta, Canada, qualitative methods were deployed. Semi-structured interviews (n=2) were conducted with surgeons, and focus groups (n=9) were conducted with seven nurses and two administrative staff to understand barriers and facilitators to the implementation of a provincial decolonization strategy. Interview questions were developed in conjunction with the Theoretical Domains Framework (TDF) and the research team. An inductive analysis derived from a Grounded Theory (GT) approach was conducted with the assistance of NVivo software. Results Knowledge and understanding of the decolonization strategy were central to implementation. When present, they acted as facilitators, but when absent or inconsistent, they were significant barriers to implementation. Specifically, clinics needed more knowledge and direction on how to deliver the decolonization strategy to patients receiving homecare; who had repeat surgeries; who required surgery during COVID-19 outbreaks. Conclusions Knowledge and understanding was a core category which summarized seven subcategories found within our GT analysis. A successful decolonization strategy will benefit from adopting further planning and development for specific patients and respiratory outbreaks such as COVID-19. Further aspects that may act as facilitators include having a champion in each clinic, regular reporting, and audit and feedback strategies. Findings from our study can provide information on the barriers and facilitators of a decolonization strategy and can be used in making the decolonization strategy successful.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| 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".