Comparison of perceived genetic-genomic knowledge of nurse educators and graduate degree nursing students
Bibliographic record
Abstract
Objective and rationale: Comparison of self-perceived genetic-genomic knowledge of nurse educators and graduate degree nursing students enrolled at a large diverse urban university in the US. Comfort level in performing genetic-genomic related tasks and its perceived relevance to nursing also explored. Practicing clinicians are expected to have Genetics and Genomics (G-G) knowledge to provide care to a client and their family with a genetic condition and faculty expected to be able to educate these practitioners.Methods: Two groups of participants asked to complete identical survey instrument, Genetics/Genomics Literacy Assessment (GGLA). Data was collected from September 2014-December 2015. Deans/Directors from university’s nursing programs (N = 17) sent introductory email with survey link and asked to forward to their faculty. APRN/DNP students at one of the university’s graduate programs sent email with survey link.Results: Fifty-three nurse educators and thirty-six graduate degree nursing students completed survey. Comparison of categorical data revealed nurse educators perceived G-G knowledge correlated to graduate degree students. Majority of participants demonstrated significantly lower correct percentages (< 55%) correct] to survey questions. Majority of participants (> 75%) attitude agree it is important for nurses to know this content and be able to teach this material. However, majority (> 75%) were not comfortable with teaching or explaining this material.Conclusions: Nurse educators need to be knowledgeable in order to educate their students who are expected to practice at advanced efficiency. A prepared nursing workforce is crucial for the translation of G-G integration into personalized precision 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".