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Record W3003903450 · doi:10.5737/23688076301915

Practical innovation: Advanced practice nurses in cancer care

2020· article· en· W3003903450 on OpenAlexaffvenueabout
Colleen Campbell, Allyson Nowell, Karen Karagheusian, Janet Giroux, Catherine Kiteley, Lorraine Martelli, Maurene McQuestion, Maureen Quinn, Yvonne P Rowe Samadhin, Melissa Touw, Lesley Moody

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2020
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsPrincess Margaret Cancer CentreKingston Health Sciences CentreCancer Care OntarioSunnybrook Health Science Centre
Fundersnot available
KeywordsDelphi methodAdvanced Practice NursesDelphiMedicineExpert opinionMedical educationNursingMEDLINEBest practiceHealthcare deliveryHealth careFamily medicinePolitical scienceComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: The objectives of this study were to gather emerging practice evidence, through consultation with Advance Practice Nurses (APN), to fill the evidence gaps in the published guidelines, Effective Use of Advanced Practice Nurses in the Delivery of Adult Cancer Services in Ontario, and to provide a set of expert panel recommendations to build a research agenda to promote the collection and publication of Level 1 and 2 evidence. METHOD: A three-step RAND/UCLA Appropriateness Methodology (RAM) modified Delphi process was used to solicit expert opinion on the use of APNs in adult cancer care in Ontario. RESULTS: Thirty-four (34) case examples of APN use were gathered. The modified Delphi process concluded with the endorsement of 30 APN role statements that were used to develop nine (9) additional recommendations regarding the use of APNs in the delivery of adult cancer care. CONCLUSION: The recommendations from this study provide direction for future research to close the current evidence gap regarding the role of APNs in cancer care delivery in Canada.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.823
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.546
Teacher spread0.428 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations10
Published2020
Admission routes3
Has abstractyes

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