Nutrition Care after Discharge from Hospital: An Exploratory Analysis from the More-2-Eat Study
Bibliographic record
Abstract
Many patients leave hospital in poor nutritional states, yet little is known about the post-discharge nutrition care in which patients are engaged. This study describes the nutrition-care activities 30-days post-discharge reported by patients and what covariates are associated with these activities. Quasi-randomly selected patients recruited from 5 medical units across Canada (n = 513) consented to 30-days post-discharge data collection with 48.5% (n = 249) completing the telephone interview. Use of nutrition care post-discharge was reported and bivariate analysis completed with relevant covariates for the two most frequently reported activities, following recommendations post-discharge or use of oral nutritional supplements (ONS). A total of 42% (n = 110) received nutrition recommendations at hospital discharge, with 65% (n = 71/110) of these participants following those recommendations; 26.5% (n = 66) were taking ONS after hospitalization. Participants who followed recommendations were more likely to report following a special diet (p = 0.002), different from before their hospitalization (p = 0.008), compared to those who received recommendations, but reported not following them. Patients taking ONS were more likely to be at nutrition risk (p < 0.0001), malnourished (p = 0.0006), taking ONS in hospital (p = 0.01), had a lower HGS (p = 0.0013; males only), and less likely to believe they were eating enough to meet their body’s needs (p = 0.005). This analysis provides new insights on nutrition-care post-discharge.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".