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Record W2328782706 · doi:10.1097/ncq.0b013e3182852f46

The Value of Bedside Shift Reporting Enhancing Nurse Surveillance, Accountability, and Patient Safety

2013· article· en· W2328782706 on OpenAlexaff
Lianne Jeffs, Ashley Acott, Elisa Simpson, Heather Campbell, Terri Irwin, Joyce Lo, Susan Beswick, Roberta Cardoso

Bibliographic record

VenueJournal of Nursing Care Quality · 2013
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsPatient safetyNursingLeverage (statistics)AccountabilityMedicineMEDLINEWorkloadPsychologyHealth careComputer science

Abstract

fetched live from OpenAlex

A study was undertaken to explore nurses' experiences and perceptions associated with implementation of bedside nurse-to-nurse shift handoff reporting. Interviews were conducted with nurses and analyzed using directed content analysis. Two themes emerged that illustrated the value of bedside shift reporting. These themes included clarifying information and intercepting errors and visualizing patients and prioritizing care. Nurse leaders can leverage study findings in their efforts to embed nurse-to-nurse bedside shift reporting in their respective organizations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.073
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.073
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.376
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), 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

Citations45
Published2013
Admission routes1
Has abstractyes

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