Bibliographic record
Abstract
Objective: It is well established that genetics plays an increasing role in healthcare. This has given rise to an ongoing discussion about the genetics competencies that healthcare personnel should possess. Nurses are key players in healthcare, and several studies have found that current genetics teaching in nursing education is insufficient. Other studies have shown that many nurses have a very limited knowledge of genetics. The objective of the present study was to gain insight into the genetics content in the Danish nursing education.Methods: We conducted a questionnaire study, involving one science lecturer from each of the 26 Danish nursing schools, asking informants about the status of genetics at their nursing school, e.g. curriculum, number of teaching lessons and exam.Results: With a response rate of 100%, we found a large variation between schools regarding the number of genetics lessons offered, ranging from two to eleven lessons. There was also a large variation with regard to curriculum. Most schools used one of two textbooks written in Danish, and classical genetics subjects such as DNA structure, protein synthesis, single gene diseases, pedigrees, and chromosome abnormalities were part of the literature curriculum in almost all schools, with variation in the level of detail. Genetics subjects of specific relevance to nursing and healthcare, such as pharmacogenetics and cancer genetics, were only part of the literature curriculum in some schools. Genetics was only a minor exam subject (if at all), and inclusion of ethical and social aspects of genetics in healthcare varied considerably.Conclusions: This study gives a unique insight into the situation regarding genetics in the Danish nursing education, and we argue that national recommendations regarding genetics teaching in nursing education are of importance in order to harness the full potential of genetics in 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".