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Record W2436496303 · doi:10.1111/jonm.12409

Factors influencing the effectiveness of audit and feedback: nurses' perceptions

2016· article· en· W2436496303 on OpenAlexafffundabout
Venessa Christina, Kathryn Baldwin, Alain Biron, Jessica Emed, Karine Lepage

Bibliographic record

VenueJournal of Nursing Management · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsMcGill University Health CentreJewish General HospitalMcGill University
FundersJewish General Hospital
KeywordsAuditNursingPerceptionAccountabilityPsychologyQuality (philosophy)MedicineBusinessAccounting

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.483
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations37
Published2016
Admission routes3
Has abstractyes

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