Exploring the International Terminology Associated with Nurses Caring for Neonates, Infants, Children, Young People and Their Families
Bibliographic record
Abstract
The terminology used for Registered Nurses specializing in caring for neonates, infants, children, young people (CYP), and their families varies globally. While many countries’ nursing students qualify as “Registered Nurses” upon completion of undergraduate education, specialist titles like “Children’s Nurses” in the United Kingdom or “Pediatric Nurses” in Italy denote expertise in CYP care. In countries like the United States and Canada, neonatal and pediatric nursing specialization typically requires postgraduate study. However, there is limited evidence on the range of international terms for nurses in this field. This expert opinion paper presents the results from a scoping survey designed to identify and catalog these terms across different countries. This exercise and the data collected were used to inform a larger cross-section study: “A Survey To map the glObal provision of children’s nUrsiNg eDucation” (the ASTOUND study). Conducted between May and August 2024, the survey collected responses from 76 participants across 34 countries representing all continents. Content analysis and descriptive statistics revealed 20 distinct terms, with “Paediatric/Pediatric Nurse” (n = 28) as the most common, followed by “Children’s Nurse” (n = 7), “Child Health Nurse” (n = 5), and others. Additional findings highlighted regional variation in terminology based on the clinical setting and population age range, underscoring historical and cultural influences on these terms. This initial survey provides a snapshot of global terminology. It underscores the need for further research, setting the stage for exploration in the ASTOUND study to map the provision of children’s nursing education worldwide.
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".