MétaCan
Menu
Back to cohort
Record W3147717476 · doi:10.1111/nhs.12836

Translating evidence‐based nursing clinical handover practice in an acute care setting: A quasi‐experimental study

2021· article· en· W3147717476 on OpenAlexaboutno aff
Adriana Hada, Lee Jones, Leanne Jack, Fiona Coyer

Bibliographic record

VenueNursing and Health Sciences · 2021
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsGeneralizability theoryMedicineAcute careAdverse effectHandoverPatient safetyIntervention (counseling)NursingOddsEmergency medicineOdds ratioClinical PracticeEvidence-based nursingFamily medicineHealth careInternal medicineAlternative medicinePsychologyLogistic regression

Abstract

fetched live from OpenAlex

Effective transfer of information during the nursing handover contributes to patient safety. This study aimed to translate the best practice nursing shift handover recommendations in an acute care setting using the Ottawa Model for Research Use and to explore its effect on patient adverse outcomes (falls, pressure injuries, and medication errors). Using a quasi-experimental design, the study was conducted in four internal medicine wards in a major tertiary hospital. A total of 88 nurses and 110 patients participated in 152 handover observations. The findings showed clinically important increases in percentages and odds of nurses' compliance with shift handover recommendations after the intervention. The patient adverse outcomes after the intervention were compared to the corresponding period of previous year. A reduction was observed for all adverse patient outcomes with incident rate ratios of 0.762 (p = 0.027) for falls, 0.624 for pressure injuries (p = 0.010), and 0.782 for medication errors (p = 0.023). Replicating this study's methodology across multiple clinical settings will increase the generalizability of findings and provide further evidence to inform nursing practice and policy.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.183
GPT teacher head0.561
Teacher spread0.378 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueNursing and Health SciencesSame topicHospital Admissions and OutcomesFrench-language works237,207