Improving nursing students research knowledge through participation in a study about nutrition, its associated factors and assessment
Bibliographic record
Abstract
Aims: The aims of this study were threefold: 1) to explore nursing students perceptions of knowledge development after participating in an actual research project; 2) to explore undernutrition and its relationship to other clinical factors; 3) to explore the user-friendliness of the Minimal Eating Observation and Nutrition Form (MEONF-II) in relation to dependency in Activities of Daily Living (ADL). Methods: A pilot study (Study 1, S1) was conducted in October 2010, including 281 patients. After extending the research protocol a second data collection (S2) was conducted in March 2011, including 236 patients (total n=517). First and third year nursing students (n=188) collected the data, during one day of their clinical practice courses by assessing three patients each in hospitals or nursing homes. Students answered questions about their experiences from participating in the study. Patient related assessments included: MEONF-II; ADL dependency (S1 and S2); insomnia; low-spiritedness; and subjective health (S2). In addition, questions about the user-friendliness of MEONF-II were included (S1 and S2). Results: Among the nursing students, 51% experienced that their knowledge about nutrition increased and 67% that their understanding for research increased by participating in the project. Out of the patients, 57% were women, 50% were almost independent, 27% had some dependency, 23% were almost totally dependent in ADL, and 48% were at moderate/ high undernutrition risk. In S2, 32% of patients had insomnia, and 46% experienced low-spiritedness. Dependency in 5-6 and 3-4 ADLs (OR, 2.439 and 2.057, respectively), compared to dependency in 0-2 ADLs, were the strongest predictors for undernutrition risk, followed by insomnia (OR 2.124). Nursing students experienced the MEONF-II as easy to understand (93%), easy to answer (94%) and relevant (94%), and the suggestions for measures to take in case of risk as relevant (95%), independent of the patients’ ADL status. Conclusions: By participating in an actual research project nursing students gets an understanding for research and tools for working with quality improvements in their future role as professional nurses. Undernutrition, mental and physical factors constitute coexisting problems in need for further investigation. Nursing students perceive the MEONF-II as user-friendly, independent of patient ADL dependency.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".