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

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.

Quality of life drives patients' preferences for secondary findings from genomic sequencing.

There is growing impetus to include measures of personal utility, the nonmedical value of information, in addition to clinical utility in health technology assessment (HTA) of genomic tests such as genomic sequencing (GS). However, personal utility and clinical utility are challenging to define and measure. This study aimed to explore what drives patients' preferences for hypothetically learning medically actionable and non-medically actionable secondary findings (SF), capturing clinical and personal utility; this may inform development of measures to evaluate patient outcomes following return of SF. Semi-structured interviews were conducted with adults with a personal or family cancer history participating in a trial of a decision aid for selection of SF from genomic sequencing (GS) ( www.GenomicsADvISER.com ). Interviews were analyzed thematically using constant comparison. Preserving health-related and non-health-related quality of life was an overarching motivator for both learning and not learning SF. Some participants perceived that learning SF would help them "have a good quality of life" through informing actions to maintain physical health or leading to psychological benefits such as emotional preparation for disease. Other participants preferred not to learn SF because results "could ruin your quality of life," such as by causing negative psychological impacts. Measuring health-related and non-health-related quality of life may capture outcomes related to clinical and personal utility of GS and SF, which have previously been challenging to measure. Without appropriate measures, generating and synthesizing evidence to evaluate genomic technologies such as GS will continue to be a challenge, and will undervalue potential benefits of GS and SF.