Surgical Site Infection Prevention: A Qualitative Analysis of an Individualized Audit and Feedback Model
Bibliographic record
Abstract
BACKGROUND: Surgical site infection (SSI) adversely affects patient outcomes and health care costs, so prevention of SSI has garnered much attention worldwide. Surgical site infection is recognized as an important quality indicator of patient care and safety. The purpose of this study was to use qualitative research methods to evaluate staff perceptions of the utility and impact of individualized audit and feedback (AF) data on SSI-related process metrics for their individual practice, as well as on overall communication and teamwork as they relate to SSI prevention. STUDY DESIGN: This study was performed in a tertiary care center, based on patients treated in the colorectal and hepatic-pancreatic-biliary surgical oncology services. Eighteen clinicians were interviewed. Analysis of interviews via comparative analysis techniques and coding strategies were used to identify themes. RESULTS: The most important finding of this study was that although nearly all participants believed that the individualized AF model was useful in effecting individual practice change as well as improving awareness and accountability around individual roles in preventing SSIs, it was not seen as a means to enable the multidisciplinary teamwork required for sustainable practice changes. Moreover, such teamwork requires a team leader. CONCLUSIONS: Provision of individualized AF data had a significant impact on promoting individual practice change. Despite this, we concluded that practice change is a shared responsibility, requiring a team leader. So, AF had little bearing on establishing a necessary multidisciplinary team approach to SSI prevention, to create more effective and sustainable practice change among an entire team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".