US neonatal intensive care unit registered dietitian nutritionists salary description and correlates: results of a survey
Bibliographic record
Abstract
Abstract Background This survey described the compensation of neonatal intensive care unit (NICU) registered dietitian nutritionists (RDNs) in the United States and examined correlates of higher salaries within this group. Methods A cross‐sectional online survey was completed in 2021 by 143 NICU RDNs from 127 US hospitals who reported hourly wage in US dollars (USD). We used initial bivariate analyses to assess the relationship of selected institution‐level and individual‐level variables to hourly wage; the rank‐sum test for binary variables; bivariate regression and Pearson correlation coefficients for continuous variables; the Kruskal–Wallis test for categorical variables. Variables with a compelling relationship to the hourly wage outcome were considered in model creation. Final model selection was based on comparisons of model fit. Results Median hourly compensation was USD 33.24 (interquartile range [IQR] 29.81, 38.49). Seven variables had a compelling bivariate relationship with hourly wage: cost of living, employer facility with a paediatric residency, employer facility with a neonatal fellowship, NICU bed: full‐time equivalents (FTE) RDN ratio, years in neonatal nutrition, having a certification and order writing privileges. In the final adjusted model ( R 2 = 0.42), three variables remained associated with increased hourly wage: higher cost of living, longer length of career in neonatal nutrition and fewer NICU beds per NICU RDN FTE. Conclusions US NICU RDNs earn similar or slightly higher wages than other US paediatric RDNs; they earn substantially less than other NICU healthcare team members. Employers need to improve compensation for NICU RDNs to incentivise their retention and recognise their additional non‐clinical responsibilities.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".