De-implementation of low-value nursing practice: a scoping review protocol
Bibliographic record
Abstract
Introduction Data from the Canadian Institute for Health Information has shown that as much as 30% of healthcare is considered low value, which can lead to poor patient outcomes due to adverse events of treatments or unwarranted secondary tests. Along with the popularity of implementation science over those years, increasing attention has been paid to de-implementation. While de-implementation has garnered increasing research across various healthcare domains, its application in the nursing field has received limited attention. The purpose of this review is to examine the progress of de-implementation research conducted in the nursing field to inform future study designs and research directions. Method We will conduct this research following the JBI scoping review methodology. Six databases (i.e., Medline (Ovid), Embase (Ovid), Web of Science, Scopus, PsychINFO (EBSCO), CINAHL(EBSCO), de-implementation focused or related journals (i.e., Implementation Science, Implementation Science Communications), and google scholar will be searched from their inception to December 1st, 2023 using keywords relate to nursing, de-implementation and low-value practice. We will include studies of all design types as long as they focus on de-implementation of low-value nursing practices, or low-value practices that require nurses’ participation within healthcare settings. Four reviewers will independently screen all titles, abstracts and full-text articles. Four reviewers will extract the data according to the appropriate checklists. Three reviewers will conduct data synthesis to explore study characteristics, de-implementation determinants and strategies, and study outcomes. Ethics and dissemination This scoping review will not include empirical data, and therefore requires no ethics approval. The results of the review will be disseminated in a peer-reviewed scientific journal and in a conference presentation.
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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 | 0.179 | 0.166 |
| Meta-epidemiology (narrow) | 0.007 | 0.008 |
| Meta-epidemiology (broad) | 0.018 | 0.013 |
| Bibliometrics | 0.025 | 0.018 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.021 |
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".