A systematic review of the use of theory in randomized controlled trials of audit and feedback
Bibliographic record
Abstract
BACKGROUND: Audit and feedback is one of the most widely used and promising interventions in implementation research, yet also one of the most variably effective. Understanding this variability has been limited in part by lack of attention to the theoretical and conceptual basis underlying audit and feedback. Examining the extent of theory use in studies of audit and feedback will yield better understanding of the causal pathways of audit and feedback effectiveness and inform efforts to optimize this important intervention. METHODS: A total of 140 studies in the 2012 Cochrane update on audit and feedback interventions were independently reviewed by two investigators. Variables were extracted related to theory use in the study design, measurement, implementation or interpretation. Theory name, associated reference, and the location of theory use as reported in the study were extracted. Theories were organized by type (e.g., education, diffusion, organization, psychology), and theory utilization was classified into seven categories (justification, intervention design, pilot testing, evaluation, predictions, post hoc, other). RESULTS: A total of 20 studies (14%) reported use of theory in any aspect of the study design, measurement, implementation or interpretation. In only 13 studies (9%) was a theory reportedly used to inform development of the intervention. A total of 18 different theories across educational, psychological, organizational and diffusion of innovation perspectives were identified. Rogers' Diffusion of Innovations and Bandura's Social Cognitive Theory were the most widely used (3.6% and 3%, respectively). CONCLUSIONS: The explicit use of theory in studies of audit and feedback was rare. A range of theories was found, but not consistency of theory use. Advancing our understanding of audit and feedback will require more attention to theoretically informed studies and intervention design.
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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 | 0.207 | 0.563 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.021 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".