Improving genetics and genomics education in the preregistration nursing curriculum: A cross-sectional survey
Bibliographic record
Abstract
• Knowledge of genetics, genomics, and epigenetics is a priority for all healthcare professionals. • An online survey identified that student nurses are inadequately prepared for competency in this aspect of their nursing practice. • Nursing schools must have curricula that include genetic and genomic content applicable to clinical practice and tailored to knowledge gaps. Registered nurses must be prepared to apply genomics-informed nursing care. To inform the development of genomic literacy curricula by evaluating preregistration nursing students’ knowledge of genetics and genomic principles. The Genomic Nursing Concept Inventory (GNCI) was administered as an anonymous, cross-sectional online survey to preregistration nursing students. The GNCI is a 31-item validated instrument for assessing knowledge of genetics and genomics considered necessary to support registered nurses’ understanding and application to clinical practice. Descriptive analysis was conducted to evaluate students' level of knowledge and understanding of key concepts. Cronbach alpha and item discrimination instrument reliability scores were calculated. The response rate was 24.1% (66/273). Correct scores for content subcategories of inheritance, genomic healthcare, genome basics, and mutations were 57%, 54%, 40%, and 36%, respectively. On average, students answered 15 of 31 GNCI items correctly, i.e., 48.4% correct responses (SD = 3.9). Preregistration nursing students demonstrated some basic genetics literacy of foundational concepts concerning genomics and genetics. However, gaps in genomic knowledge across all three preregistration training years were noted.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".