Defining Priorities for Canadian PICU Family Presence Policies Under Changing Contexts: A Qualitative Focus Group Study
Bibliographic record
Abstract
INTRODUCTION: Recognizing the importance of parental presence for seriously ill children's well-being, many pediatric intensive care units (PICUs) have adopted policies encouraging family presence. However, PICU family presence policies remain varied, with gaps in policy development and implementation across Canadian hospitals. We aimed to determine patient, family, clinician, and policymaker-identified priorities for family presence policies under baseline and emergency (e.g., pandemics, disease outbreaks) contexts. METHODS: Between January and August 2023, we conducted focus groups with PICU youth, families, clinicians, and policymakers. Using the Theoretical Domains Framework (TDF) and inductive analysis, we explored key themes. RESULTS: Seven focus groups included youth (n = 4), family members (n = 7), clinicians (n = 14), and policymakers (n = 4). Nearly all TDF domains were significant, leading to three primary categories: policy development (e.g., transparency, adaptability), implementation (e.g., communication, roles), and future lessons. DISCUSSION: Key priorities included bedside access for two people, sibling presence, and flexible, equitable policies developed with diverse community engagement.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".