P0199 / #2113: DESIGN AND IMPLEMENTATION OF A NURSE-LED CRITICAL CARE OUTREACH SERVICE IN A TERTIARY CARE PEDIATRIC HOSPITAL
Bibliographic record
Abstract
Aims & Objectives: Literature and government standards support the concept that intensive care is a modality, not a location. Implementation of a Critical Care Outreach Nurse (CCON) will ensure that staff, patients, and families have access to critical care expertise throughout the hospital campus, and improve patient outcomes across the continuum of care. Further, we hope that increased support and collaboration will lead to ward staff feeling more confident and empowered in their work. Methods: Literature review and benchmarking were completed, as well as a review of a similar program in our centre from 2006-2007. Stakeholder analysis and engagement were done with a special focus on nurses and nurse-leaders on inpatient wards. Senior Pediatric Intensive Care Unit (PICU) nurses were hired into the CCON role and trained according to best practices in resuscitation and emergency response. Acknowledging unique circumstances on our inpatient wards of high staff turnover and relatively inexperienced nurses, additional education in trauma-informed practice and psychological safety was added. Results: At present, we are readying for implementation of the CCON role. Rates of out-of-PICU cardiac arrest, in-hospital mortality, and readmission to PICU post-discharge are already known and will continue to be followed. We also plan to collect post-interaction feedback from staff in order to assess their experiences with the CCON. Conclusions: While most critical care response systems primarily target improved patient outcomes, we hope that a nurse-led approach will improve patient outcomes as well as enhance staff satisfaction and inter-departmental collaboration.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".