Reporting and design elements of audit and feedback interventions: a secondary review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Audit and feedback (A&F) is a frequently used intervention aiming to support implementation of research evidence into clinical practice with positive, yet variable, effects. Our understanding of effective A&F has been limited by poor reporting and intervention heterogeneity. Our objective was to describe the extent of these issues. METHODS: Using a secondary review of A&F interventions and a consensus-based process to identify modifiable A&F elements, we examined intervention descriptions in 140 trials of A&F to quantify reporting limitations and describe the interventions. RESULTS: We identified 17 modifiable A&F intervention elements; 14 were examined to quantify reporting limitations and all 17 were used to describe the interventions. Clear reporting of the elements ranged from 56% to 97% with a median of 89%. There was considerable variation in A&F interventions with 51% for individual providers only, 92% targeting behaviour change and 79% targeting processes of care, 64% performed by the provider group and 81% reporting aggregate patient data. CONCLUSIONS: Our process identified 17 A&F design elements, demonstrated gaps in reporting and helped understand the degree of variation in A&F interventions.
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.
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 | 0.064 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it