Genomic competence among nurses: A spotlight on ethics
Bibliographic record
Abstract
BackgroundGlobally, ethics is recognized as a critical component for ensuring equitable and sustainable genomic healthcare. However, prior research has largely overlooked the ethical aspects when assessing nurses' genomic competence.Research aimThis study aimed to assess the genomic competence of nurses in Finland, with a specific focus on their perspectives regarding ethics in genomics.Research designThis was a cross-sectional study conducted among registered nurses in Finland.Participants and research contextThe data were collected via an online survey between October 30 and December 31, 2023, using the Canadian Adaptation of the Genetics Genomics Nursing Practice Survey (GGNPS-CA), which evaluates attitudes, receptivity, confidence, competency, knowledge, social systems, and the decision adoption process in genomics with ethical dimensions. A total of 234 registered nurses participated.Ethical considerationsThe study was ethically approved by the Ethics Committee of the Tampere Region, statement number 46/2023.ResultsWhile 76.8% of nurses rated their self-assessed understanding of genomics as poor, their actual Knowledge Score was relatively good (mean 9.12/12, SD 1.44). In addition, nurses reported limited understanding of the ethical issues associated with genomics, particularly concerning equity. The majority (59.4%) believed it was very important for nurses to become more educated on ethical issues, while 28.6% considered it somewhat important.ConclusionsThe findings suggest a strong perceived need among nurses for further education in both genomics and its ethical implications. The discrepancy between self-assessed and actual knowledge may reflect low confidence, which was additionally reported in the ethical issues. Low confidence is possibly influenced by the early stage of genomics integration into 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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".