Current State of Nutrition Education in Pediatric Critical Care Medicine Fellowship Programs in the United States and Canada
Bibliographic record
Abstract
OBJECTIVES: To assess the current state of nutrition education provided during pediatric critical care medicine fellowship. DESIGN: Cross-sectional survey. SETTING: Program directors and fellows from pediatric critical care medicine fellowship programs in America and Canada. SUBJECTS: Seventy current pediatric critical care medicine fellows and twenty-five pediatric critical care medicine fellowship program directors were invited to participate. INTERVENTIONS: Participants were asked demographic questions related to their fellowship programs, currently utilized teaching methods, perceptions regarding adequacy and effectiveness of current nutrition education, and levels of fellow independence, comfort, confidence, and expectations in caring for the nutritional needs of patients. MEASUREMENTS AND MAIN RESULTS: Surveys were sent to randomly selected program directors and fellows enrolled in pediatric critical care medicine fellowship programs in America and Canada. Twenty program directors (80%) and 60 fellows (86%) responded. Ninety-five percent of programs (19/20) delivered a formal nutrition curriculum; no curriculum was longer than 5 hours per academic year. Self-reported fellow comfort with nutrition topics did not improve over the course of fellowship (p = 0.03), with the exception of nutritional aspects of special diets. Sixty-five percent of programs did not hold fellows responsible for writing daily parenteral nutrition prescriptions. There was an inverse relationship between total number of fellows in a pediatric critical care medicine program and levels of comfort in ability to provide parenteral nutrition support (p = 0.01). Program directors perceived their nutritional curriculum to be more effective than did their fellows (p ≤ 0.001). CONCLUSIONS: Nutrition education was reported as highly underrepresented in pediatric critical care medicine fellowship curricula. The majority of programs rely on allied health care professionals to prescribe parenteral nutrition, which may influence trainee independence in the provision of nutritional therapies. Improving the format of current nutrition curriculums, by relying on more active teaching methods, may improve the delivery and efficacy of nutrition education. The impact of novel training interventions on improving the competency and safety of enteral and parenteral nutrition delivery in the PICU must be further examined.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".