How to Design Effective Audit and Feedback Interventions With Nurses
Bibliographic record
Abstract
OBJECTIVE: To propose practical hypotheses on audit and feedback that support the effectiveness with nurses. BACKGROUND: Audit and feedback interventions have been mainly studied with physicians; however, the processes have been practiced by nurses for years. Nurses' response may differ from that of physicians and other healthcare disciplines because of their roles, power, and the configuration of nursing activities. METHODS: A comparative analysis of the Clinical Performance Feedback Intervention Theory was conducted using nursing-specific empirical data from: 1) a mixed-methods systematic review and 2) a pilot study of audit and feedback with a team of primary care nurses. RESULTS: Researchers hypothesize that audit and feedback interventions are more effective when: 1) feedback emphasizes how it relates to the relational aspect of nursing; 2) indicators are measured and reported at team level; and 3) feedback is provided in a way that highlights benefits to nurses' practice, such as the potential to reduce workload. CONCLUSION: These proposed hypotheses provide concrete guidance to researchers and managers for an effective use of audit and feedback as a quality improvement strategy with nurses.
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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 | 0.088 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".