Prevalence of current nutrition care practices for disease-related malnutrition in Canadian hospitals
Bibliographic record
Abstract
Disease-related malnutrition is common in hospital patients. The Health Standards Organization Canadian Malnutrition Prevention, Detection, and Treatment Standard was published in 2021. The purpose of this study was to determine the current state of nutrition care in hospitals prior to implementation of the Standard. An online survey was distributed to hospitals across Canada via email. A representative reported on nutrition best practices based on the Standard at the hospital level. Descriptive and bivariate statistics were completed for selected variables based on size and type of hospital. One hundred and forty-three responses from nine provinces were received (56% community, 23% academic, and 21% other). Malnutrition risk screening was being completed on admission in 74% ( n = 106/142) of hospitals, although not all units participated in screening all patients. Nutrition-focused physical exam is completed as part of a nutrition assessment in 74% ( n = 101/139) of sites. Flagging a malnutrition diagnosis ( n = 38/104) and physician documentation (18/136) were sporadic. Academic and medium (100–499 beds) and large hospitals (500+ beds) were more likely to have a physician document a malnutrition diagnosis. Some, but not all, best practices are occurring in Canadian hospitals on a regular basis. This demonstrates a need for continued knowledge mobilization of the Standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".