Genetics and Genomics Teaching in Nursing Programs in a Latin American Country
Bibliographic record
Abstract
Although the importance of genetics and genomics in nursing education has been widely recognized, surveys carried out in several countries show that these subjects are still limited in nursing undergraduate programs. In Latin America, the teaching of genetics and genomics in nursing programs has never been previously documented. Considering this scenario, we aimed to investigate how genetics and genomics have been taught in undergraduate nursing programs in Brazil. A total of 138 undergraduate nursing program coordinators and 49 faculty members were recruited to participate in this cross-sectional study. After IRB approval, data were collected using an online survey, covering curriculum design, faculty credentials, genetics and/or genomics teaching, as well as their impressions regarding the document “Essential Nursing Competencies and Curricula Guidelines for Genetics and Genomics”. Genetics is taught in most of the investigated courses (67.3%), mainly by biologists (77.6%), with master’s degree (83.7%), and with the syllabus mainly focused on molecular biology. More instructors agreed with Competency 2 (C2) which refers to advocating for clients’ access to desired genetic/genomic services and/or resources including support groups as well as C23 which refer to using health promotion/disease prevention practices that incorporate knowledge of genetic and genomic risk factors, than coordinators. That is, the participants’ type of appointment (instructors vs. coordinators) had a significant effect on their agreement level with competencies C2 (χ2 = 6.23, p = 0.041) and C23 (χ2 = 9.36, p = 0.007). Overall, a higher number of participants with both master’s and Ph.D. degrees significantly agreed with competencies C2, C4, which refer to incorporating genetic and genomic technologies and information into registered nurse practice, and C5—demonstrating in practice the importance of tailoring genetic and genomic information and services to clients based on their culture, religion, knowledge level, literacy, and preferred language, when compared to those with Ph.D. only, and those with a master’s degree only (χ2 = 8.73, p = 0.033; χ2 = 8.61, p = 0.033; χ2 = 8.61, p = 0.033, respectively). Our results support reflections on ways to prepare the nursing workforce to deliver personalized nursing care. Additionally, they can be an aid in establishing guidelines for the undergraduate nursing curricula in Brazil and in other Portuguese-speaking countries, as well as in Latin America.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".