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Record W4322013364 · doi:10.1097/nnd.0000000000000960

Utilizing Handoff Reporting Tools to Improve the Novice Nurse's Workflow

2023· article· en· W4322013364 on OpenAlexaff
Kristelle Garcia

Bibliographic record

VenueJournal for Nurses in Professional Development · 2023
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsASTER
Fundersnot available
KeywordsWorkflowTask (project management)NursingMedical educationHandoverMEDLINEPsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

The novice nurse is task oriented and requires guidance to recognize connections in clinical practice. Novice nurses must learn to prioritize, organize, and differentiate between information that is "nice-to-know" versus "need-to-know" to deliver competent nursing care. Nursing literature makes evident that utilizing communication frameworks increases the delivery of clear communication and improves patient outcomes. Novice nurses require a comprehensive handoff-reporting tool to prompt the novice to engage in critical thinking and facilitate communication within their 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 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)0.035
metaresearch head score (Gemma)0.138
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: none
Teacher disagreement score0.035
Threshold uncertainty score0.187

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.138
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0020.000
Scholarly communication0.0040.005
Open science0.0030.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.003

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 head0.051
GPT teacher head0.399
Teacher spread0.349 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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Same venueJournal for Nurses in Professional DevelopmentSame topicHospital Admissions and OutcomesFrench-language works237,207