A theoretical framework for interaction of nursing discipline with genetics and genomics
Bibliographic record
Abstract
BACKGROUND: Since the completion of the Human Genome Project, health science has been strongly influenced by the advances in genetics and genomics. However, the progress of embracing genetics and genomics into nursing discipline is limited. One of the main barriers is lack of understanding on the relevancy of genetics and genomics to nursing discipline. OBJECTIVES: This paper aims to synthesize and develop a theoretical framework for the interaction of nursing discipline with genetics and genomics. METHODS: Through content analysis and constant comparative method, a theoretical framework was developed from synthesis of the studies regarding nursing and genetics/genomics indexed in multiple English and Chinese databases. RESULTS: Four main theoretical statements were constructed in the framework: 1) There are three ways to show how genetics and genomics can influence nursing discipline: a new specialty, new technologies and a new lens; 2) The significant contribution of nursing discipline to genetics and genomics lies in how nurses could focus on the association between human responses and genes and how nurses could advocate for their clients in the genetic and genomic era; 3) A paradigm shift occurs after a constant interaction of nursing discipline with genetics and genomics; 4) Implementation strategies could be used to facilitate the integration of genetics and genomics to nursing discipline and advance the paradigm shift. CONCLUSIONS: The framework will help to understand the relationship between nursing discipline and genetics and genomics and implicate the future studies integrating genetics and genomic science into nursing discipline.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".