Exploring Literacy and Perceptions of Genomics Among Undergraduate Nursing Students and Faculty: A Mixed Methods Study
Bibliographic record
Abstract
As the single largest health care profession in Canada, nurses have a remarkable opportunity to shape the implementation of genomic health care, and will need a solid foundation in genetic and genomic knowledge to do so (Calzone et al., 2010; Canadian Institute for Health Information, 2016). In the early 2000s, a dedicated group of nurse leaders provided recommendations for genetic nursing in Canada (Bottorff et al., 2004). Since that time, the literature and guidelines from Canadian nursing organizations suggest that there has been little progress in the implementation of these recommendations. A mixed-methods explanatory sequential design combining a cross-sectional administration of a survey and thematic analysis of focus group discussion was used to answer the following research questions: Quantitative - How do nursing undergraduate students and faculty perform on the Genomic Nursing Concept Inventory (GNCI)? What individual socio-demographic characteristics and previous experiences with genetics are associated with performance on the GNCI? Qualitative - What barriers and facilitators to the addition of genetic and genomic content into undergraduate nursing curricula are identified by nursing undergraduate students and faculty? Mixed Methods - How do the barriers and facilitators associated with the addition of genetic and genomic knowledge into undergraduate nursing curriculum broaden understanding and provide context for the scores on the GNCI? The average percent correct on the GNCI for the 220 participants was 45%, which is comparable to results of sample US students and faculty. Characteristics associated with higher performance on the GNCI included older age, attending site A, not being female, having taken a genetics course, a previous degree, and having a positive attitude towards nurses learning about genetics. A list of barriers and facilitators was developed, along with eight themes (gaps in understanding; complexity; gaps in curriculum; lack of role models; scope; role; application; and relevance) describing the general sense of becoming “stuck” when discussing integration of genetics into the nursing curriculum. Clear implications emerged from the integration of the quantitative results and qualitative findings, which can be used to focus future research and efforts to advance the inclusion of genetic and genomic knowledge in undergraduate nursing curricula in Canada.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".