Role of the neonatal registered dietitian nutritionist in Canada: A description of staffing and a comparison to practices in the United States
Bibliographic record
Abstract
BACKGROUND: Neonatal registered dietitian nutritionists (RDNs) are critical members of the neonatal intensive care unit (NICU) team. Ideal RDN staffing levels are unknown. Current staffing levels of neonatal RDNs in Canadian NICUs have not been recently reported. The objective of this study was to describe neonatal RDN staffing and responsibilities in Canada and contrast these findings with those of neonatal RDNs in the United States. METHODS: An online cross-sectional neonatal RDN survey was performed in the Fall of 2021 to collect hospital-level and individual-RDN-level data. Descriptive statistics were performed to summarize Canadian neonatal RDN staffing levels and responsibilities and compared with US findings. RESULTS: Canadian RDNs reported a median staffing ratio of 25.3 NICU beds per RDN full-time equivalent, with neonatal RDNs reporting a desired 31% increase in staffing. The majority of Canadian NICUs (n = 20/24) reported having a dedicated space to prepare infant feeds away from bedside. Canadian neonatal RDNs reported wanting to expand their responsibilities in research, administration, and education. Canadian neonatal RDNs reported a higher rate of order writing privileges as compared with that of US neonatal RDNs. CONCLUSION: Canadian neonatal RDNs reported a desired increase in their staffing levels. Neonatal RDNs have the potential to expand their professional role but require additional staffing, dedicated time, and compensation to support this. Further research determining the optimal neonatal RDN staffing ratio to maximize patient outcomes is required.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".