Factors influencing the effectiveness of audit and feedback: nurses' perceptions
Bibliographic record
Abstract
AIM: To explore the perceptions of nurses in an acute care setting on factors influencing the effectiveness of audit and feedback. BACKGROUND: Audit and feedback is widely used and recommended in nursing to promote evidence-based practice and to improve care quality. Yet the literature has shown a limited to modest effect at most. Audit and feedback will continue to be unreliable until we learn what influences its effectiveness. METHOD: A qualitative study was conducted using individual, semi-structured interviews with 14 registered nurses in an acute care teaching hospital in Montreal, Canada. RESULTS: Three themes were identified: the relevance of audit and feedback, particularly understanding the purpose of audit and feedback and the prioritisation of audit criteria; the audit and feedback process, including its timing and feedback characteristics; and individual factors, such as personality and perceived accountability. CONCLUSION: According to participants, they were likely to have a better response to audit and feedback when they perceived that it was relevant and that the process fitted their preferences. IMPLICATIONS FOR NURSING MANAGEMENT: This study benefits nursing leaders and managers involved in quality improvement by providing a better understanding of nurses' perceptions on how best to use audit and feedback as a strategy to promote evidence-based practice.
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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 | 0.022 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".