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Record W4413420236 · doi:10.1097/njh.0000000000001155

Integrating the Clinical Nurse Specialist Into a Multi-State Hospice in a Large Healthcare System

2025· article· en· W4413420236 on OpenAlexaff
J. Lee, Melissa Robinson

Bibliographic record

VenueJournal of Hospice and Palliative Nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsProvidence Health Care
Fundersnot available
KeywordsTimelineClinical nurse specialistNursingPalliative careMedicineHealth careClinical PracticePolitical science

Abstract

fetched live from OpenAlex

Advanced Practice Registered Nurses are increasingly recognized for their role and impact in hospice and palliative care. The Clinical Nurse Specialist (CNS) role is emerging as uniquely suitable for practice and leadership in this setting. The integration of a CNS into the practice of a large, multistate Hospice and Palliative Care program is discussed in this article. A timeline is provided with 4 phases of integration with priorities identified and specific strategies that were implemented to overcome challenges during the process. The value of CNS practice to the organization is highlighted in 3 practice exemplars, showing return on investment in terms of increased patient safety and implementation of evidence-based practice for home infusions, developing nurse confidence through complex case reviews, and reducing staff injuries through developing a Safe Patient Handling program.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.124
GPT teacher head0.510
Teacher spread0.386 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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