Nursing strategies to address health disparities in genomics-informed care: a scoping review
Bibliographic record
Abstract
OBJECTIVE: The objective of this review was to map the available global evidence on strategies that nurses can use to facilitate genomics-informed health care to address health disparities to inform the development of a research and action agenda. INTRODUCTION: The integration of genomics into health care is improving patient outcomes through better prevention, diagnostics, and treatment; however, scholars have noted concerns with widening health disparities. Nurses work across the health system and can address health disparities from a clinical, research, education, policy, and leadership perspective. To do this, a comprehensive understanding of existing genomics-informed strategies is required. INCLUSION CRITERIA: Published (qualitative, quantitative, mixed methods studies; systematic and literature reviews; and text and opinion papers) and unpublished (gray) literature that focused on genomics-informed nursing strategies to address health disparities over the past 10 years were included. No limitations were placed on language. METHODS: The review was conducted in accordance with the JBI methodology for scoping reviews. A search was undertaken on May 25, 2023, across 5 databases: MEDLINE (Ovid), Embase, Cochrane Library (Ovid), APA PsycINFO (EBSCOhost), and CINAHL (EBSCOhost). Gray literature was searched through websites, including the International Society of Nurses in Genetics and the Global Genomics Nursing Alliance. Abstracts, titles, and full texts were screened by 2 or more independent reviewers. Data were extracted using a data extraction tool. The coded data were analyzed by 2 or more independent reviewers using conventional content analysis, and the summarized results are presented using descriptive statistics and evidence tables. RESULTS: In total, we screened 818 records and 31 were included in the review. The most common years of publication were 2019 (n=5, 16%), 2020 (n=5, 16%), and 2021 (n=5, 16%). Most papers came from the United States (n=25, 81%) followed by the Netherlands (n=3, 10%), United Kingdom (n=1, 3%), Tanzania (n=1, 3%), and written from a global perspective (n=1, 3%). Nearly half the papers discussed cancer-related conditions (n=14, 45%) and most of the others did not specify a disease or condition (n=12, 39%). In terms of population, nurse clinicians were mentioned the most frequently (n=16, 52%) followed by nurse researchers, scholars, or scientists (n=8, 26%). The patient population varied, with African American patients or communities (n=7, 23%) and racial or ethnic minorities (n=6, 19%) discussed most frequently. The majority of equity issues focused on inequitable access to genetic and genomics health services among ethnic and racial groups (n=14, 45%), individuals with lower educational attainment or health literacy (n=6, 19%), individuals with lower socioeconomic status (n=3, 10%), migrants (n=3, 10%), individuals with lack of insurance coverage (n=2, 6%), individuals living in rural or remote areas (n=1, 3%), and individuals of older age (n=1, 3%). Root causes contributing to health disparity issues varied at the patient, provider, and system levels. Strategies were grouped into 2 categories: those to prepare the nursing workforce and those nurses can implement in practice. We further categorized the strategies by domains of practice, including clinical practice, education, research, policy advocacy, and leadership. Papers that mentioned strategies focused on preparing the nursing workforce were largely related to the education domain (n=16, 52%), while papers that mentioned strategies that nurses can implement were mostly related to clinical practice (n=19, 61%). CONCLUSIONS: Nurses in all domains of practice can draw on the identified strategies to address health disparities related to genomics in health care. We found a notable lack of intervention and evaluation studies exploring the impact on health and equity outcomes. Additional research informed by implementation science that measures health outcomes is needed to identify best practices. SUPPLEMENTAL DIGITAL CONTENT: A French-language version of the abstract of this review is available: http://links.lww.com/SRX/A65 .
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".