Trends in behaviour change techniques for implementing and de-Implementing healthcare practices using audit and feedback
Bibliographic record
Abstract
Abstract Background Audit and feedback (A&F) is a widely used quality improvement strategy to modify healthcare professionals’ practice. However, there is considerable variation in how A&F is applied and in its effectiveness. Investigating this variation, in terms of differences in the behaviour change techniques (BCTs) interventions employ, and why it occurs may provide insights for optimising intervention design. This study, therefore, explored associations between which BCT are used in A&F interventions, behaviour change direction (implementation vs. de-implementation), and target behaviour type. Methods An exploratory secondary analysis was conducted on data from 261 randomized trials of A&F interventions, originally extracted as part of a Cochrane systematic review. Regression analyses investigated whether different BCTs were used for implementation versus de-implementation. These analyses were repeated in subgroups of different behaviour types (e.g., prescribing, testing/examinations). Parallel analyses aggregated data at the study level to assess the robustness of findings. Results Analyses on the whole sample demonstrated that the same BCTs were used to implement and de-implement target behaviours in A&F interventions. Subgroup analyses identified potential associations between specific BCTs and implementation direction within certain behaviour types: social comparison with implementation of treatment decisions/actions; education (unspecified) with implementation of testing/examinations; social support (unspecified) with implementation of prescribing; and feedback on outcome of behaviour with de-implementation of treatment decisions/actions. However, these associations were not consistently replicated across the main and parallel analyses, and may reflect prevailing design practices or methodological artefacts rather than genuine differences in BCT selection. Conclusions This study demonstrated that, overall, A&F interventions utilise similar BCTs regardless of whether behaviours are being implemented or de-implemented. However, exploratory subgroup analyses suggest that tailoring interventions through selectively using certain BCTs for either implementation or de-implementation, depending on the type of behaviour being acted on, may warrant further investigation. Future research should test these hypotheses using theory-informed intervention designs and robust methods, to determine whether current patterns of BCT use reflect true differences in intervention effectiveness.
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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 | 0.021 | 0.151 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".