Leveraging de-implementation science to promote infection prevention and stewardship: a roadmap and practical examples (Part II of II)
Bibliographic record
Abstract
De-implementation of established practices is a common challenge in infection prevention and antimicrobial stewardship and a necessary part of the life cycle of healthcare quality improvement programs. Promoting de-implementation of ineffective antimicrobial use and increasingly of low-value diagnostic testing are cornerstones of stewardship practice. Principles of de-implementation science and the interplay of implementation and de-implementation are discussed in part I of this Society for Healthcare Epidemiology of America White Paper Series.In this second part of the series, we discuss a process for applying principles of de-implementation science in infection prevention and stewardship and then review some real-world examples and case studies, including a national blood culture shortage, contact precautions, and surgical and dental prophylaxis. We use these examples to demonstrate how barriers and facilitators can be mapped to evidence-informed implementation/de-implementation strategies to promote efforts to reduce low-value, ineffective, or out-of-date practices. These real-world examples highlight the need for infection prevention and stewardship programs to adapt to changing evidence, contexts, and conditions. Although barriers to practice change are often a bit different, de-implementation can sometimes be thought of as the implementation of a new program-but the new program aims to stop rather than start doing something.As the saying goes, sometimes less really is more. Medicine and public health have a strong action bias and a strong aversion to risk and uncertainty. Although our best intentions may point us to implementing more interventions, often, the best medicine instead dictates that we do less, or nothing at all. Leveraging principles of de-implementation science can help move healthcare in the right direction when interventions are low-value, ineffective, or no longer needed.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".