Becoming Food Aware in Hospital: A Narrative Review to Advance the Culture of Nutrition Care in Hospitals
Bibliographic record
Abstract
The Nutrition Care in Canadian Hospitals (2010-2013) study identified the prevalence of malnutrition on admission to medical and surgical wards as 45%. Nutrition practices in the eighteen hospitals, including diagnosis, treatment and monitoring of malnourished patients, were ad hoc. This lack of a systematic approach has demonstrated the need for the development of improved processes and knowledge translation of practices aimed to advance the culture of nutrition care in hospitals. A narrative review was conducted to identify literature that focused on improved care processes and strategies to promote the nutrition care culture. The key finding was that a multi-level approach is needed to address this complex issue. The organization, staff, patients and their families need to be part of the solution to hospital malnutrition. A variety of strategies to promote the change in nutrition culture have been proposed in the literature, and these are summarized as examples for others to consider. Examples of strategies at the organizational level include developing policies to support change, use of a screening tool, protecting mealtimes, investing in food and additional personnel (healthcare aides, practical nurses and/or diet technicians) to assist patients at mealtimes. Training for hospital staff raises awareness of the issue, but also helps them to identify their role and how it can be modified to improve nutrition care. Patients and families need to be aware of the importance of food to their recovery and how they can advocate for their needs while in hospital, as well as post-hospitalization. It is anticipated that a multi-level approach that promotes being "food aware" for all involved will help hospitals to achieve patient-centred care with respect to nutrition.
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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 | 0.009 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".