Pushing boundaries in paediatric intensive care: training as a paediatric retrieval nurse practitioner
Bibliographic record
Abstract
Traditionally in the UK, the transportation of the critically ill child to a paediatric intensive care unit has been carried out by a medically led team of doctors and nurses. However, in countries such as the USA and Canada, appropriately trained nurse practitioners have proven to be competent in the transportation of these vulnerable children. This nurse-led team model has also been shown to be successful in the speciality of neonatal care in the UK. The impact of changes in the National Health Service (NHS) has led to an increased demand for the transportation of the child requiring paediatric intensive or high-dependency care, the lifting of restrictions on nursing practice and the reduction of doctors' hours in keeping with the European Working Time Directive. This has led to one NHS Trust in the UK developing the role of paediatric retrieval nurse practitioners (RNP): nurses who lead the retrieval team. The purpose of this article is to describe a pilot initiative to develop the role of RNPs. The comprehensive process of recruitment, training and assessment of competency will be detailed. Personal reflection on the project will also explore the pertinent nursing issues around; role impact and definition, conflict and change management, communication, legislation and personal and professional growth. Recommendations for future initiatives will also be explored.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".