Nurses' Perceptions Regarding the Prevalence, Detection, and Causes of Malnutrition in Canadian Hospitals
Bibliographic record
Abstract
OBJECTIVES: Given the high prevalence of malnutrition in hospitalized patients, nurses frequently encounter patients with significantly impaired nutrition status. The objective of this study was to determine nurses' attitudes and perceptions regarding the prevalence, detection, and causes of malnutrition in Canadian tertiary care and community hospitals. MATERIALS AND METHODS: In this descriptive study, a survey that focused on guidelines for nutrition support of hospitalized patients was completed by Canadian nurses working on medical and surgical wards in 11 hospitals participating in the Canadian Malnutrition Task Force study. RESULTS: The survey was completed by 346 of 723 nurses (response rate 48%). Over 50% of nurses underestimated the documented prevalence of malnutrition in hospitalized patients. Nurses considered identification of malnourished patients very relevant (mean 8.4 on a 10-point scale) and would integrate a 3-question nutrition screen into their admission histories (92.5%). Nurses perceived lack of assistance with eating as a significant contributor to hospital malnutrition (17% felt this was a major contributor). While only 39% of nurses reported access to nutrition-related education, 92% were interested in receiving this form of updating. CONCLUSIONS: Nurses consider nutrition assessment important and relevant and require access to training to improve their capacity to detect malnutrition in their patients. Nurses are vital to the nutrition care of hospitalized patients and are well positioned to screen for nutrition risk and assist in nutrition management. The role of nurses in nutrition care needs to be linked to hospital policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".