Building a Genomics-Informed Nursing Workforce: Recommendations for Oncology Nursing Practice and Beyond
Bibliographic record
Abstract
BACKGROUND: Genomics is a foundational element of precision health and can be used to identify inherited cancers, cancer related risks, therapeutic decisions, and to address health disparities. However, there are structural barriers across the cancer care continuum, including an underprepared nursing workforce, long wait times for service, and inadequate policy infrastructure that limit equitable access to the benefits of genomic discoveries. These barriers have persisted for decades, yet they are modifiable. Two distinct waves of efforts to integrate genomics into nursing practice are analyzed. Drawing on research and observations during these waves, this discussion paper explores additional approaches to accelerate workforce development and health system transformation. RESULTS: Three recommendations for a third wave of efforts to integrate genomics are explored. (1) Collaborate across the domains of nursing practice, professions, and sectors to reset priorities in response to emerging evidence, (2) Education in leadership, policy and practice for rapid scale-up of workforce and health system transformation, and (3) Create a research framework that generates evidence to guide nursing practice. CONCLUSIONS: Preparing nurses to lead and practice at the forefront of innovation requires concerted efforts by nurses in all five domains of practice and can optimize health outcomes. Leveraging nursing as a global profession with new strategies can advance genomics-informed nursing.
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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 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.008 | 0.018 |
| Research integrity | 0.022 | 0.017 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".