National Association of Neonatal Nurse Practitioners (NANNP) Workforce Survey
Bibliographic record
Abstract
BACKGROUND: As an integral member of a healthcare team, neonatal nurse practitioners (NNPs) provide care in a variety of settings that include but are not limited to all levels of inpatient care, transport, acute and chronic care settings; delivery rooms; and outpatient care settings. Anecdotal evidence indicates that responsibilities, practice environment, and workload vary widely between regions and practice settings. PURPOSE: Historically, the supply of neonatal nurse practitioners has rarely met the demand for services, although needs vary by region at any given time. Because the NNP role is a collaborative one, a shortage of NNPs leaves a gap in the team approach to care. In 2011, the National Association of Neonatal Nurse Practitioners (NANNP) commissioned the first national study of the current NNP workforce in the United States and Canada. In an effort to further explore the NNP workforce population, the NANNP Council partnered with the National Certification Corporation to perform a second workforce survey of NNPs in the spring of 2014. FINDINGS/RESULTS: The online survey was conducted between March and April 2014. The goal of the study was to describe the demographics, practice environment, scope of responsibilities, benefits and reimbursement, and job satisfaction for the current NNP workforce. IMPLICATIONS FOR PRACTICE/RESEARCH: Key areas of concern identified by the 2014 Neonatal Nurse Practitioner Workforce Survey include an aging workforce; the need for NNP faculty; inadequate staffing ratios; the lack of downtime during prolonged shifts; and the need to assisting practices in developing competency and mentoring programs.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".