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.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".