Evaluation of nutritional literacy among undergraduate nursing students
Bibliographic record
Abstract
Background/Objective: It is difficult to describe or quantify the extensiveness and importance of the nurses’ role in direct patient care. Although Registered Dietitian Nutritionists (RDN) are available, it is often the nurse who is tasked with monitoring a patient’s nutritional status and carrying out nutrition interventions to optimize oral intake such as providing meals and oral supplements, as well as administering enteral and parenteral nutrition. Therefore, it is critical that nurses have an understanding of nutrition to promote good health. Although fundamental nursing courses stress the nurse’s role in promoting healthy nutrition as a means to prevent disease and assist patients in sustaining wellness, there is no standardized nursing curriculum enforcing an independent nutrition course within nursing programs.Methods: A quantitative methodology was used to explore the level of nutritional awareness as measured by the Nutritional Literacy Assessment Instrument (NLit) among BSN nursing students in a public, 4-year institution in the Mid-West of the United States. Nutritional literacy, as defined by Gibbs et al., students’ knowledge of food and associated impact on nutrition was evaluated. Results: The data indicated that 70% of participants had completed an undergraduate nutrition course, with the highest performance on the Nutritional Literacy Survey within the understanding of food groups. Over 80% of respondents were found to have a likelihood of good nutrition literacy. Although no statistical significance was found between a pre-requisite Nutrition course and/or an integrated Nursing Nutrition course and Nutritional Literacy, statistical significance was found when examining student classification (sophomores versus seniors) impact on Nutritional Literacy scores.Conclusions: Promoting the integration of nutrition concepts across a variety of courses within the nursing curriculum will assist the student to utilize clinical judgment in the application and synthesis of nutritional concepts in relation to patient health and wellness.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".