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Record W2408440335 · doi:10.1188/16.cjon.327-331

Use of a Point-of-Care Tool to Improve Nurse Practitioner BRCA Knowledge

2016· article· en· W2408440335 on OpenAlexaboutno aff
Mary Alison Smania

Bibliographic record

VenueClinical journal of oncology nursing · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBreast cancerCancerBRCA mutationOncologyNurse practitionersPopulationFamily medicineGynecologyInternal medicineHealth careEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Women who have been identified with a BRCA mutation benefit from a multidisciplinary, individualized medical evaluation to reduce their risk of developing cancers. Identifying women who would gain from testing for BRCA mutations is essential. Nurse practitioners (NPs) as primary care providers are important members of the healthcare team and are instrumental in identifying and referring women for testing. However, studies have shown that NPs lack knowledge about and confidence in identifying women at risk. OBJECTIVES: This project was undertaken to increase NP knowledge about assessing women at risk for the BRCA mutation and determining whether such testing is appropriate. This was accomplished through a BRCA risk assessment tool developed as a mobile health technology (MHT) application using the Ontario Family History Assessment Tool, one of the tools recommended by the U.S. Preventive Services Task Force in its guidelines on BRCA-related cancer risk assessment, genetic counseling, and genetic testing to assist primary care providers in the assessment of women. METHODS: NPs attending an NP conference in the midwestern United States completed pre-test, post-test, and satisfaction surveys regarding use of the MHT application. The application included a point-of-care tool and educational information. FINDINGS: The participants demonstrated increased knowledge from pre- to post-test after use of the MHT application, with an overall positive evaluation.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.050
GPT teacher head0.431
Teacher spread0.382 · 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 designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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