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Record W4380291567 · doi:10.21037/apm-22-1121

Growth and development of oncology nursing in North America

2023· review· en· W4380291567 on OpenAlexafffundabout
Lisa Kennedy Sheldon, Reanne Booker

Bibliographic record

VenueAnnals of Palliative Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsUniversity of VictoriaAlberta Health Services
FundersAssociation canadienne des infirmières en oncologieCanadian Nurses FoundationAlberta Cancer FoundationPfizer
KeywordsMedicinePaceOncology nursingSpecialtyNursingPalliative careProfessional developmentOncologyNurse educationMedical educationFamily medicine

Abstract

fetched live from OpenAlex

The specialty of oncology nursing has been evolving in North America for nearly a century, keeping pace with the rapid and dynamic developments in cancer care. This narrative review outlines the history and development of oncology nursing in North America with a focus on the United States and Canada. The review highlights the important contributions that specialized oncology nurses have made to the care of people affected by cancer from time of diagnosis through treatment, follow-up and survivorship care, as well as palliative, end-of-life, and bereavement care. Keeping pace with the rapid evolution of cancer treatments throughout the last century, nursing roles have similarly evolved to meet the need for more specialized training and education. This paper discusses the growth of nursing roles, including advanced practice and navigator roles. In addition, the paper outlines the development of professional oncology nursing organizations and societies that have been established to help guide the profession with best practices, standards, and competencies. Finally, the paper discusses new challenges and opportunities regarding the access, availability, and delivery of cancer care that will shape future development of the specialty. Oncology nurses will continue to be integral to the provision of high-quality, comprehensive cancer care as clinicians, educators, researchers, and leaders.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.937
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.521
GPT teacher head0.563
Teacher spread0.042 · 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.

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

Citations5
Published2023
Admission routes3
Has abstractyes

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