A Peer and Volunteer Program for Patients and Their Families in the Pediatric Intensive Care Unit: A Pilot Program Evaluation
Bibliographic record
Abstract
Introduction: Patients in the pediatric intensive care unit (PICU) are at risk of developing long-term morbidities following recovery from their critical illness. One such health outcome is called post-intensive care syndrome (PICS). PICS in pediatrics may be mitigated by interventions that facilitate adjustment to the PICU setting. Methods: The PICU implemented a two-pronged Peer and Volunteer (P/V) Program to help: (a) families adjust to the PICU experience with the support of a peer mentor (PM); and (b) patients receive non-medical interaction from trained volunteers (V). We designed a mixed-methods program evaluation targeting perspectives and feedback from PICU families and healthcare professionals (HCPs). Results: All stakeholder groups agreed that the PICU P/V Program was a valuable resource for PICU patients and their families. HCPs reported that they lack both time and training to provide regular developmental care to patients. However, the P/V Program may influence both families' and HCP's confidence in their ability to offer non-medical interaction to children in the PICU. Discussion: Important initial and on-going strengths and barriers to successful implementation were identified, including the need to clarify roles and intervention scope. The program evaluation served as a change management strategy and also helped to identify both areas for improvement and strategies for on-going sustainability. HCP's exposure to the program and modeling by PMs may have helped HCPs to feel that it is within their job description and capacity to provide emotional support and guidance to families.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".