109 The use of theories and frameworks to understand and address the reduction of low-value healthcare practices: a scoping review
Bibliographic record
Abstract
Background There is recognition that the overuse of procedures, testing and medications strains the healthcare system financially and can cause unnecessary stress and harm for patients. In recent years, several initiatives—such as Choosing Wisely—have targeted the reduction or elimination of low-value practices in healthcare. Researchers have begun to use theory or develop frameworks to elucidate the dynamics of de-implementation and support efforts to reduce low-value practices. The purpose of this scoping review was to identify and characterize the use of theories and frameworks to understand and address the reduction of low-value healthcare practices. Methods We conducted a systematic review of MEDLINE, EMBASE, CINAHL and Scopus databases from inception to January 2018. Building on previous research, 44 key terms were used to search the literature. To be included, papers had to present an explicit theoretical approach or framework. The database searches identified 725 unique articles for which titles and abstracts were screened for inclusion; 74 items were selected for full text review. Results Forty studies met the inclusion criteria. Over 50% of included articles were published in the last two years. Of studies which used a theoretical approach, the majority used psychological theories, such as the Theory of Planned Behaviour or applied behavioural science concepts to develop interventions. The included studies demonstrated a maturating of the use of theory in this field, with a progression from the use of classic theories to multi-theory frameworks to dual processing models. Theories or frameworks were used primarily to identify barriers and facilitators or develop conceptual clarity. Articles used theory to understand how provider decision-making, knowledge or perceptions of social pressure play a role in overuse. The majority of studies addressed low-value care at the provider level. Antibiotic overuse, polypharmacy and appropriate prescribing were the practices targeted most frequently in the included studies. Conclusions De-implementation is an emerging field of research. This scoping review was the first to review the use of theory in efforts to reduce low-value practice. The results of this review can provide direction and insight for future primary research in the use of theory to support de-implementation and reduction of low-value healthcare practices.
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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 | 0.048 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.039 | 0.030 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".