A Nursing Survey on Nutritional Care Practices in French‐Speaking Pediatric Intensive Care Units
Bibliographic record
Abstract
OBJECTIVES: Malnutrition in critically ill children contributes to morbidity and mortality. The French-speaking pediatric intensive care nutrition group (NutriSIP) aims to promote optimal nutrition through education and research. METHODS: The NutriSIP-designed NutriRéa-Ped study included a cross-sectional survey. This 62-item survey was sent to the nursing teams of all of the French-speaking pediatric intensive care units (PICUs) to evaluate nurses' nutrition knowledge and practices. One nurse per PICU was asked to answer and describe the practices of their team. RESULTS: Of 44 PICUs, 40 responded in Algeria, Belgium, Canada, France, Lebanon, Luxemburg, and Switzerland. The majority considered nutrition as a priority care but only 12 of the 40 (30%) had a nutrition support team, 26 of the 40 (65%) had written nutrition protocols, and 19 of 39 (49%) nursing teams felt confident with the nutrition goals. Nursing staff generally did not know how to determine nutritional requirements or to interpret malnutrition indices. They were also unaware of reduced preoperative fasting times and fast-track concepts. In 17 of 35 (49%) PICUs, the target start time for enteral feeding was within the first 24 hours; however, frequent interruptions occurred because of neuromuscular blockade, fasting for extubation or surgery, and high gastric residual volumes. Combined pediatric neonatal intensive care units were less likely to perform systematic nutritional assessment and to start enteral nutrition rapidly. CONCLUSIONS: We found a large variation in nursing practices around nutrition, exacerbated by the lack of nutritional guidelines but also because of the inadequate nursing knowledge around nutritional factors. These findings encourage the NutriSIP to improve nutrition through focused education programs and research.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".