Does telling people what they have been doing change what they do? A systematic review of the effects of audit and feedback
Bibliographic record
Abstract
BACKGROUND: Many people advocate audit and feedback as a strategy for improving professional practice. The main results of an update of a Cochrane review on the effects of audit and feedback are reported. DATA SOURCES: The Cochrane Effective Practice and Organisation of Care Group's register up to January 2004 was searched. Randomised trials of audit and feedback that reported objectively measured professional practice in a healthcare setting or healthcare outcomes were included. REVIEW METHODS: Data were independently extracted and the quality of studies were assessed by two reviewers. Quantitative, visual and qualitative analyses were undertaken. MAIN RESULTS: 118 trials are included in the review. In the primary analysis, 88 comparisons from 72 studies were included that compared any intervention in which audit and feedback was a component to no intervention. For dichotomous outcomes, the median-adjusted risk difference of compliance with desired practice was 5% (interquartile range 3-11). For continuous outcomes, the median-adjusted percentage change relative to control was 16% (interquartile range 5-37). Low baseline compliance with recommended practice and higher intensity of audit and feedback appeared to predict the effectiveness of audit and feedback. CONCLUSIONS: Audit and feedback can be effective in improving professional practice. The effects are generally small to moderate. The absolute effects of audit and feedback are likely to be larger when baseline adherence to recommended practice is low and intensity of audit and feedback is high.
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 | 0.032 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".