Malnutrition care in hospitalized pediatric inpatients: comparison of perceptions and experiences across two pediatric academic health sciences centres
Bibliographic record
Abstract
Malnutrition affects up to one in three Canadian children admitted to hospital. Awareness among pediatric healthcare providers (HCPs) of the prevalence and impacts of hospitalized malnutrition is critical for optimal management. The purpose of this study was to determine perceptions of malnutrition among pediatric HCP across two major academic health sciences centres, and to determine how the use of a standardized pediatric nutritional screening tool at one institution affects responses. Between 2020 and 2022, 192 HCPs representing nursing, dietetics, medicine, and other allied health were surveyed across McMaster Children's Hospital and The Hospital for Sick Children. 38% of respondents from both centres perceived rates of malnutrition between approximately one in three patients. Perceptions of the need for nutritional screening, assessment, and management were similar between centres. All respondents identified the need for better communication of hospitalized malnutrition status to community providers at discharge, and resource limitations affecting nutritional management of pediatric inpatients. This study represents the largest and most diverse survey of inpatient pediatric HCPs to date. We demonstrate high rates of baseline knowledge of hospital malnutrition, ongoing resource challenges, and the need for a systematic approach to pediatric nutritional management.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".