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Record W4389779848 · doi:10.1371/journal.pone.0295914

Genomics-informed nursing strategies and health equity: A scoping review protocol

2023· review· en· W4389779848 on OpenAlexafffund
Dzifa Dordunoo, Jacqueline Limoges, Patrick Chiu, Rebecca Puddester, Lindsay Carlsson, April Pike

Bibliographic record

VenuePLoS ONE · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsPrincess Margaret Cancer CentreMemorial University of NewfoundlandAthabasca UniversityUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaGenome AlbertaGenome Canada
KeywordsCINAHLHealth careGrey literatureMEDLINEContext (archaeology)MedicineHealth equityWorkforceGenomicsNursingPublic healthPolitical sciencePsychological intervention

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this scoping review is to map the available evidence on strategies that nurses can use to facilitate genomics-informed healthcare to address health disparities. INTRODUCTION: Advancements in genomics over the last two decades have led to an increase in the delivery of genomics-informed health care. Although the integration of genomics into health care services continues to enhance patient outcomes, access to genomic technologies is not equitable, exacerbating existing health disparities amongst certain populations. As the largest portion of the health workforce, nurses play a critical role in the delivery of equitable genomics-informed care. However, little is known about how nurses can help address health disparities within the context of genomics-informed health care. A review of the literature will provide the necessary foundation to identify promising practices, policy, and knowledge gaps for further areas of inquiry. INCLUSION CRITERIA: We will include papers that explore strategies that nurses can undertake to facilitate genomics-informed care to address health disparities. METHODS: This review will be conducted using JBI methodology for scoping reviews. We will search electronic databases including MEDLINE (OVID), EMBASE, Cochrane Library, PsychInfo, and CINAHL for quantitative and qualitative studies, systematic reviews and grey literature. Theses, books, and unavailable full-text papers will be excluded. The search will be limited to papers from 2013 and beyond. Two reviewers will screen titles and abstracts followed by full-text and disagreements will be resolved by a third reviewer. We will use a data extraction tool using Microsoft Excel and analyse data using descriptive statistics and conventional content analysis. Findings will be presented in the form of evidence tables and a narrative summary. We will report findings using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). DISCUSSION: Genomics will continue to transform all aspects of health care across the wellness continuum from prevention, assessment, diagnosis, management, treatment, and palliative care. The identification of nursing strategies to address health disparities will build the foundation for policy and practice to ensure that the integration of genomic technologies benefits everyone.

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)0.134
metaresearch head score (Gemma)0.115
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.134
Threshold uncertainty score0.707

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1340.115
Meta-epidemiology (narrow)0.0050.006
Meta-epidemiology (broad)0.0140.015
Bibliometrics0.0290.023
Science and technology studies0.0070.006
Scholarly communication0.0110.011
Open science0.0080.010
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.0750.016

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 head0.272
GPT teacher head0.485
Teacher spread0.213 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations6
Published2023
Admission routes2
Has abstractyes

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