Becoming Food Aware in Hospital: A Narrative Review of Best Practices for a Multi‐level Approach to Improve the Culture of Nutrition in Hospital
Bibliographic record
Abstract
Malnutrition can develop in the community, contributing to hospital admission and may persist post‐discharge thus increasing the chance of readmission. The Nutrition Care in Canadian Hospitals (2010‐13) study identified the prevalence of malnutrition on admission to medical and surgical wards as 45%, with older patients more likely to be malnourished. Nutrition practices in these hospitals including diagnosis, treatment, monitoring and primary care follow up were ad hoc. This lack of a systematic approach has demonstrated the need for knowledge translation of best practice. A multi‐level approach is proposed to address this complex issue. Care practices discussed are based on grey literature and evidence to date, which encompasses training and methods of action for hospital staff including hospital management. Patients and their families also need to be included in this approach as being aware of the importance of nutrition can make a patient more involved in their recovery. This approach encompasses but goes beyond the UK Seven Steps to End Malnutrition and the Australian Eight‐Step Interdisciplinary Framework on the Prevention of Undernutrition. Improvements to organizational practices include use of screening tools, protected mealtimes, additional nursing or nutritional assistants during mealtimes and discharge communications to support successful transition to the community. Everyone in the hospital should be aware of the importance of nutrition and their role in the optimization of nutrition care. Overall, this proposed multi‐level approach aims to improve the culture of nutrition in hospitals so issues are addressed before patients are discharged. Funder: Technology Evaluation in the Elderly Network
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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 | 0.021 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".