Implementation of the Neonatal Nurse Practitioner Role in a Community Hospital's Labor, Delivery, and Level 1 Postpartum Unit
Bibliographic record
Abstract
BACKGROUND: A level 1 community hospital with a labor, delivery, recovery, and postpartum (LDRP) unit delivering over 2800 babies per year was operating without dedicated neonatal resuscitation and stabilization support. PURPOSE: With lack of funding and space to provide an onsite level 2 neonatal intensive care unit (NICU), a position was created to provide neonatal nurse practitioner (NNP) coverage to support the LDRP unit. METHOD: The article describes the innovative solution of having an NNP team rotate from a regional neonatal intensive care program to a busy community LDRP unit. The presence of the NNP supported the development and integration of the advanced practice nursing role with interdisciplinary team members in both the LDRP and the emergency department. RESULTS: The NNP was able to provide expertise, leadership, and mentorship for neonatal resuscitation and stabilization as well as education and consultation on neonatal care. In addition to the services provided by the NNP for infant's requiring acute care, the NNP provided transitional support for those infants who remained with their mothers in the LDRP unit. Furthermore, time required by the neonatal transport team to stabilize babies before transport to the NICU was decreased with NNP presence. IMPLICATIONS FOR PRACTICE: The divergence from practice of the traditional NNP clinical role in the NICU setting to more of a consultant and nursing leader has proven to be a valued role at the community hospital. IMPLICATIONS FOR RESEARCH: A solid economic analysis of the cost-effectiveness of the NNP role in this community hospital is warranted.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".