Active change interventions to de-implement low-value healthcare practices: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: There is recognition that the overuse of procedures, tests and medications strains the healthcare system financially and can cause unnecessary stress and harm to patients. In recent years, several initiatives have targeted the reduction or elimination of low-value practices in healthcare. Research suggests that passive interventions, such as the publication of guidelines, are often not sufficient to change behaviour and that active change interventions - interventions which actively implement strategies to change practices - are required to effect significant, sustained practice change. The purpose of this scoping review is to identify and characterise studies of active change interventions designed to reduce or eliminate low-value healthcare practices. METHODS: We will conduct a review of MEDLINE, EMBASE, CINAHL and Scopus databases from inception. Building on previous research, 40 key terms will be used to search literature. The screening process will be conducted separately by two researchers, with discrepancies resolved by a third. Empirical studies of active change interventions used to reduce or eliminate low-value practices will be included. Descriptive statistics and thematic analysis will be used to categorise the characteristics of the studies. ETHICS AND DISSEMINATION: Ethics approval is not required for this study. This scoping review will provide insights into the impact of several characteristics of active change interventions, including the number of interventions (single-faceted or multifaceted) and the level of implementation (individual or organisational). These results can provide guidance and direction for future research in de-implementation. The results will be disseminated through presentations at national and international conferences and the publication of a manuscript.
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 | 0.033 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.007 |
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; both teacher heads agree on what is shown here.
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".