Pediatric Critical Care Nursing Research Priorities—Initiating International Dialogue
Bibliographic record
Abstract
OBJECTIVE: To identify and prioritize research questions of concern to the practice of pediatric critical care nursing practice. DESIGN: One-day consensus conference. By using a conceptual framework by Benner et al describing domains of practice in critical care nursing, nine international nurse researchers presented state-of-the-art lectures. Each identified knowledge gaps in their assigned practice domain and then poised three research questions to fill that gap. Then, meeting participants prioritized the proposed research questions using an interactive multivoting process. SETTING: Seventh World Congress on Pediatric Intensive and Critical Care in Istanbul, Turkey. PARTICIPANTS: Pediatric critical care nurses and nurse scientists attending the open consensus meeting. INTERVENTIONS: Systematic review, gap analysis, and interactive multivoting. MEASUREMENTS AND MAIN RESULTS: The participants prioritized 27 nursing research questions in nine content domains. The top four research questions were 1) identifying nursing interventions that directly impact the child and family's experience during the withdrawal of life support, 2) evaluating the long-term psychosocial impact of a child's critical illness on family outcomes, 3) articulating core nursing competencies that prevent unstable situations from deteriorating into crises, and 4) describing the level of nursing education and experience in pediatric critical care that has a protective effect on the mortality and morbidity of critically ill children. CONCLUSIONS: The consensus meeting was effective in organizing pediatric critical care nursing knowledge, identifying knowledge gaps and in prioritizing nursing research initiatives that could be used to advance nursing science across world regions.
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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 | 0.302 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.008 | 0.032 |
| Research integrity | 0.032 | 0.039 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".