Measured and perceived effects of audit and feedback on nursing performance: A mixed methods systematic review
Bibliographic record
Abstract
Abstract Background: The use of audit and feedback in health care has been shown to have generally positive effects with medical teams, but little is known about its effects on nursing care. The purpose of this systematic review was to examine the evidence of measured and perceived effects of such interventions on nursing performance. Methods: We used a mixed-methods systematic review design with thematic and narrative synthesis. Studies reporting quantitative and qualitative data on the effects of feedback interventions specific to nursing care were considered for inclusion. Studies were appraised for quality using the Mixed Methods Appraisal Tool. Quantitative and qualitative data were summarized in narrative and tabular form and were synthetized using the Joanna Briggs Institute segregated methodologies approach. Results: Thirty-one studies published between 1995 and 2021 were included. Thirteen quantitative studies provided evidence on measured effects and 18 qualitative studies provided evidence on perceived effects. The quantitative studies, the majority of which had low to moderate methodological quality, reported highly variable effects of audit and feedback. The characteristics of most of the audit and feedback interventions were poorly aligned with the recommendations developed by the experts and were not theoretically supported. Overall, the qualitative data demonstrated that nurses perceived several negative aspects in the way audit and feedback interventions were conducted, while recognizing the relevance of secondary use of the data to support improved care. Conclusions: Considering the practical benefits of using this type of intervention, we see in these results an important opportunity for action to improve the design and implementation of audit and feedback with nurses. Registration: PROSPERO CRD42018104973
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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 | 0.097 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".