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Record W2027843845 · doi:10.5737/23688076252144149

Prospective roles for Canadian oncology nurses in breast cancer rapid diagnostic clinics

2015· article· en· W2027843845 on OpenAlexaffvenueabout
Margareth Santos Zanchetta, Christine Maheu, Lorena Baku, Pete Wedderburn, Manon Lemonde

Bibliographic record

VenueCanadian Oncology Nursing Journal · 2015
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsOntario Tech UniversityMcMaster UniversityPrincess Margaret Cancer CentreCancer Care Ontario
Fundersnot available
KeywordsMedicineBreast cancerOncologyInternal medicineCancerFamily medicine

Abstract

fetched live from OpenAlex

The introduction of rapid diagnostic clinics for breast cancer increases oncology nurses’ (ONs) responsibility for patient education and coordination of multidisciplinary care. Developed as an outcome of the E-Mentorship Oncology Nursing Program, this paper proposes new roles for these nurses to respond effectively and competently to such diagnostic innovation. The Oslo Manual Conceptual Framework of Innovation inspired the idea of change in prospective ONs’ roles, corroborated by the Canadian Association of Nurses in Oncology’s Standards of Practice and Competencies. New roles for ONs that are informed by the domain of information dynamics and evidence-based care are proposed. The adoption of this diagnostic innovation provides a base for the widespread incorporation of the aforementioned standards and redesigned innovative in ONs care.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.173
Threshold uncertainty score0.589

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0270.009
Scholarly communication0.0080.003
Open science0.0030.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0060.001

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 head0.071
GPT teacher head0.483
Teacher spread0.412 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2015
Admission routes3
Has abstractyes

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