Providing quality nutrition care in acute care hospitals: perspectives of nutrition care personnel
Bibliographic record
Abstract
BACKGROUND: Malnutrition is common in acute care hospitals worldwide and nutritional status can deteriorate during hospitalisation. The aim of the present qualitative study was to identify enablers and challenges and, specifically, the activities, processes and resources, from the perspective of nutrition care personnel, required to provide quality nutrition care. METHODS: Eight hospitals participating in the Nutrition Care in Canadian Hospitals study provided focus group data (n = 8 focus groups; 91 participants; dietitians, dietetic interns, diet technicians and menu clerks), which were analysed thematically. RESULTS: Five themes emerged from the data: (i) developing a nutrition culture, where nutrition practice is considered important to recovery of patients and teams work together to achieve nutrition goals; (ii) using effective tools, such as screening, evidence-based protocols, quality, timely and accurate patient information, and appropriate and quality food; (iii) creating effective systems to support delivery of care, such as communications, food production and delivery; (iv) being responsive to care needs, via flexible food systems, appropriate menus and meal supplements, up to date clinical care and including patient and family in the care processes; and (v) uniting the right person with the right task, by delineating roles, training staff, providing sufficient time to undertake these important tasks and holding staff accountable for their care. CONCLUSIONS: The findings of the present study are consistent with other work and provide guidance towards improving the nutrition culture in hospitals. Further empirical work on how to support successful implementation of nutrition care processes is needed.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".