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Record W2275831274 · doi:10.1188/14.cjon.193-198

Gastrointestinal Nurse Navigation

2014· review· en· W2275831274 on OpenAlexaff
Mary May, Coralyn Woldhuis, Wendy K. Taylor, Laurence E. McCahill

Bibliographic record

VenueClinical journal of oncology nursing · 2014
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsAdvantage Forensics (Canada)
Fundersnot available
KeywordsMedicineBenchmarkingMultidisciplinary approachNursingMultidisciplinary teamContext (archaeology)Health careQuality (philosophy)Transparency (behavior)Management

Abstract

fetched live from OpenAlex

Gastrointestinal (GI) cancer is the second most frequent cancer diagnosis in the United States, and the care for patients with GI cancer is multifaceted, with each clinical encounter impacting patients' overall experience. Patients and families often navigate this complicated journey on their own with limited resources and knowledge; therefore, innovative, patient-centered, and quality-focused programs must be developed. The purpose of this article is to discuss the development of GI nurse navigators (NNs) and the important role they have in providing coordinated evidence-based cancer care and in the benchmarking of quality metrics to allow more transparency and improve GI cancer care. This article provides a foundation for developing a GI NN role within the context of a newly developed multidisciplinary GI cancer program, and identifies the importance of tracking specific quality metrics. This innovative model is useful for healthcare organizations and nursing practice because it identifies the importance of a nurse in the navigator role, as well as highlights the numerous functions the NN can provide to the GI multidisciplinary team and patients.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.401
GPT teacher head0.633
Teacher spread0.231 · 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 designOther design
Domainnot available
GenreReview

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

Citations7
Published2014
Admission routes1
Has abstractyes

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