Racial, Ethnic, and Gender Composition Among Neonatal Nurse Practitioner Faculty Ranks
Bibliographic record
Abstract
BACKGROUND: Despite increases in nursing faculty diversity, representation is lacking in positions of higher faculty rank. Challenges for minority faculty include decreased awareness of promotion standards, less mentoring, and increased stress from being the sole representative of their respective underrepresented population. METHODS: The purpose of this study was to determine the racial, ethnic, and gender composition of neonatal nurse practitioner (NNP) faculty in the United States. A nonexperimental survey was sent to all accredited NNP programs to describe demographics of NNP faculty in the United State. RESULTS: Of the 128 survey participants, 84% self-identified as White. Forty-eight of the participants ranked Professor or Associate professor were White. In contrast, all other races only had 8 respondents who were of the higher faculty ranks. There were only 2 male participants; one identified as full professor and one as associate professor. CONCLUSION: Limitations of this project included a small sample size leading to an inability to determine statistical significance. Previous evidence supports decreased diversity in higher faculty rank in other healthcare providers and the results of this study add to that body of literature. Barriers to increased diversification need to be rectified to ensure health equity to all patients.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".