Precision Health and Nursing: Seeing the Familiar in the Foreign
Bibliographic record
Abstract
Precision health is the integration of personal genomic data with biological, environmental, behavioral, and other information relevant to the care of a patient. Genetics and genomics are essential components of precision health. Genetics is the study of the effects of individual genes, and genomics is the study of all the components of the genome and interactions between genes, environmental factors, and other psychosocial and cultural factors. Knowledge about the role of genetics and genomics on health outcomes has increased substantially since the completion of the human genome project in 2003. Insights about genetics and genomics obtained from bench science are now having positive clinical implications on patient health outcomes. Nurses have the potential to make distinct contributions to precision health due to their unique role in the health care system. In this article, we discuss gaps in the development of precision health in nursing and how nursing can expand the definition of precision health to actualize its potential. Precision health plays a role in nursing practice. Understanding this connection positions nurses to incorporate genetic and genomic knowledge into their nursing practice.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".