The big six: key principles for effective use of Behavior substitution in interventions to de-implement low-value care
Bibliographic record
Abstract
ABSTRACT: Healthcare professionals provide care to help patients; however, sometimes that care is of low value - at best ineffective and at worst harmful. To address this, recent frameworks provide guidance for developing and investigating de-implementation interventions; yet little attention has been devoted to identifying what strategies are most effective for de-implementation. In this paper, we discuss Behavior substitution, a strategy whereby an unwanted behavior is replaced with a wanted behavior, thereby making it hypothetically easier to reduce or stop the unwanted behavior. We discuss why Behavior substitution may be a useful de-implementation strategy, and why it may not be suitable for all circumstances. On the basis of the body of knowledge in behavioral science, we propose a list of principles to consider when selecting a substitute behavior for a de-implementation intervention. Applying these principles should increase the likelihood that this technique will be effective in reducing low-value care.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".