A snapshot of nurses’ understanding, perceptions and comfort level of genomics
Bibliographic record
Abstract
Objective: The primary aim of this study explored holistic nurses’ self-perceived genomic knowledge, perceptions, attitude and comfort of genomics. A second aim compared results to previous findings of nurse educators and advanced degree practicing registered nurses’ genomic knowledge utilizing the same survey instruments.Methods: Design: Recruitment of participants, through the American Holistic Nurses Association (AHNA), was achieved via an anonymous Survey Monkey link of the Genetic and Genomic Literacy Assessment (GGLA) survey. The GGLA survey comprised three aspects: Self-Perceived Genomic Knowledge Survey; Perceptions and Attitudes about Genomics Integration into Nursing Practice Survey and the Comfort Level of Genomics Survey. Method: The GGLA survey link was made available via the AHNA newsletter.Results: Holistic nurses (n = 41) self-perceived genomic knowledge level demonstrated a knowledge base gap in their comprehension and ability to explain genomic concepts to their patients. Majority of holistic nurses were significantly not comfortable with their genomic knowledge (90% or greater). Comparison with nurse educators (n = 53) and advanced degree practicing registered nurses’ (n = 36) genomic knowledge provided additional insight.Conclusions: A significant majority of nurses are unprepared to adopt genomics into their practice whilst experiencing a lack comfort and confidence. The global success of nursing practice resides with its’ practitioners being fully informed and competent with all required competencies, especially if nursing is to remain prevalent within personalized healthcare.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".