Selecting De‐Implementation Strategies and Designing Interventions
Bibliographic record
Abstract
Following the activities described in the previous chapters, you are now ready to (finally) start designing your intervention. For budding and seasoned de-implementers alike, there may have been a temptation to skip ahead to this chapter. And those of you working ‘at the coal face’, on the ground, with the lived experience and expertise, you undoubtedly already have a list of several possible de-implementation strategies worth considering. But there is a reason that this chapter about selecting de-implementation strategies and designing interventions is the ninth rather than the first chapter. It is important not to rush to solutions, but instead to first consider barriers and enablers and then match the choice of strategies to those best addressing identified barriers and enablers. Now that you are ready to start designing, this chapter describes 10 general principles and key steps for selecting de-implementation strategies to enable you to draw from state-of-the-art tools and the broader understanding of how best to de-implement low-value care.
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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 | 0.087 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".