Perceived genetic knowledge among pre-licensure undergraduate nursing students
Bibliographic record
Abstract
Objective : This study assessed the perceived retention of genetic knowledge of pre-licensure undergraduate nursing students who received a stand-alone genetics course. Methods : Design: Two analyses of total score were of interest: 1) Assessment of retention of knowledge of education group at sophomore level [n = 62; 2013], junior level [n = 60; 2014] and senior level [n = 42; in 2015] and 2) Comparison of the education group [n = 62] to a control group who learned genetic content that was woven into their clinical courses [n = 74]. Methods: Data were analysed using analysis of variance (ANOVA), as the total scores were approximately normally distributed. p -values less than or equal to alpha = 0.05 were considered statistically significant. Some subjects in the assessment of retention knowledge over time remain the same. Results : The education group had a statistically significantly higher total score than the control group: mean ± standard deviation = 70.1 ± 13.8 vs. 54.2 ± 19.6, respectively for education and control groups; p -value < .001. Although education clearly had an impact on total score, the perceived knowledge was not retained over the years: average total scores of 70.1 in 2013 to 67.2 in 2014 and 61.6 in 2015; p -value = .006. Conclusions : Education has a significant effect on perceived knowledge, yet maintaining that knowledge base requires reiteration of the content through-out the curriculum. Clinical Relevance: Nurse educators’ need to be able to integrate genetic/genomic competencies into nursing curricula and reinforce the content to ensure nursing students are able to retain and utilize this knowledge when in 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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".