Racial and Ethnic Composition of Neonatal Nurse Practitioner Faculty and Students in the United States
Bibliographic record
Abstract
BACKGROUND: Neonatal nurse practitioners have a strong presence in the neonatal intensive care unit and are primed to lead efforts to induce change related to health disparities. Underrepresented minority nurse practitioners offer valuable perspectives in the care of underrepresented minority patients. However, there remains a current racial and ethnic discordance between neonatal providers and patients. Efforts to eliminate health disparities must begin before nursing school. The current racial and ethnic composition of neonatal nurse practitioner faculty in comparison to students in the United States is unknown. PURPOSE: The purpose of this study was to determine the racial and ethnic composition of neonatal nurse practitioner faculty and students in the United States and contrast this data with available data for the racial and ethnic composition of the neonatal intensive care unit patient population. METHODS: This cross-sectional study used a nonexperimental survey to describe the racial and ethnic composition of neonatal nurse practitioner faculty and students in the United States. RESULTS: There was no significant difference in the racial and ethnic composition between neonatal nurse practitioner faculty and students. There were significant differences for all race distributions between neonatal nurse practitioner students and neonatal intensive care unit admissions. IMPLICATIONS FOR PRACTICE AND RESEARCH: The discordance between neonatal nurse practitioner students and neonates in the neonatal intensive care unit is important in addressing disparities and begins before nursing school. Identification of barriers and strategies for recruitment and retention of underrepresented minority nursing students and faculty is needed. VIDEO ABSTRACT AVAILABLE AT: https://journals.lww.com/advancesinneonatalcare/pages/video.aspx?v=62.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".