Nursing-Sensitive Outcomes among Patients Cared for in Paediatric Intensive Care Units: A Scoping Review
Bibliographic record
Abstract
Measuring the effectiveness of nursing interventions in intensive care units has been established as a priority. However, little is reported about the paediatric population. The aims of this study were (a) to map the state of the art of the science in the field of nursing-sensitive outcomes (NSOs) in paediatric intensive care units (PICUs) and (b) to identify all reported NSOs documented to date in PICUs by also describing their metrics. A scoping review was conducted by following the framework proposed by Arksey and O’Malley. Fifty-eight articles were included. Publications were mainly authored in the United States and Canada (n = 28, 48.3%), and the majority (n = 30, 51.7%) had an observational design. A total of 46 NSOs were documented. The most reported were related to the clinical (n = 83), followed by safety (n = 41) and functional (n = 18) domains. Regarding their metrics, the majority of NSOs were measured in their occurrence using quantitative single measures, and a few validated tools were used to a lesser extent. No NSOs were reported in the perceptual domain. Nursing care of critically ill children encompasses three levels: improvement in clinical performance, as measured by clinical outcomes; assurance of patient care safety, as measured by safety outcomes; and promotion of fundamental care needs, as measured by functional outcomes. Perceptual outcomes deserve to 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 | 0.020 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".