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Record W4322773072 · doi:10.5430/jnep.v13n6p24

Leveraging technology to prepare for a successful Magnet® virtual site visit

2023· article· en· W4322773072 on OpenAlexvenueno aff
Kathy Arthurs, Becky Chalupa

Bibliographic record

VenueJournal of Nursing Education and Practice · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
FundersHouston Methodist Research Institute
KeywordsCredentialingWork (physics)PandemicNursingExcellencePublic relationsBest practiceBusinessPlan (archaeology)Medical educationCoronavirus disease 2019 (COVID-19)Political scienceMedicineEngineeringGeography

Abstract

fetched live from OpenAlex

Magnet Site Visits are part of the comprehensive review process for organizations seeking the American Nurses Credentialing Center’s (ANCC) prestigious Magnet® recognition for nursing excellence. In 2021, the ANCC’s Magnet Recognition Program® began offering the option for an onsite or Virtual Site Visit (VSV) for domestic and international organizations. These visits are the culmination of years of work that showcase the best in nursing practice and interprofessional collaboration. Chief Nursing Officers (CNOs) and Magnet Program Directors (MPDs) collaborate to strategically plan these visits. This article will describe a Magnet® recognized community hospital’s preparation for a VSV during the height of the COVID-19 pandemic. It will include development of partnerships with Information Technology Specialists, planning and logistics for meetings, development and implementation of a comprehensive education plan, and the overall orchestration of a VSV. Strategies will address the engagement of nurses and leaders amidst the challenges of the COVID-19 pandemic. With strategic planning, effective change management, and the adoption of technology, MPDs may gain confidence to facilitate a Magnet Site Visit virtually or in-person.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.347

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.118
GPT teacher head0.541
Teacher spread0.423 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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