Reinvigorating stagnant science: implementation laboratories and a meta-laboratory to efficiently advance the science of audit and feedback
Bibliographic record
Abstract
Audit and feedback (A&F) is a commonly used quality improvement (QI) approach. A Cochrane review indicates that A&F is generally effective and leads to modest improvements in professional practice but with considerable variation in the observed effects. While we have some understanding of factors that enhance the effects of A&F, further research needs to explore when A&F is most likely to be effective and how to optimise it. To do this, we need to move away from two-arm trials of A&F compared with control in favour of head-to-head trials of different ways of providing A&F. This paper describes implementation laboratories involving collaborations between healthcare organisations providing A&F at scale, and researchers, to embed head-to-head trials into routine QI programmes. This can improve effectiveness while producing generalisable knowledge about how to optimise A&F. We also describe an international meta-laboratory that aims to maximise cross-laboratory learning and facilitate coordination of A&F research.
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 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 | 0.759 | 0.847 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.020 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.022 | 0.041 |
| Open science | 0.011 | 0.019 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".