Critical Care and Emergency Department Nurses’ Perceptions and Recommendations Regarding Risks, Challenges, and Facilitators of Family Presence During Resuscitation
Bibliographic record
Abstract
BACKGROUND: Family presence during resuscitation (FPDR) represents a vital yet complex aspect of critical care, blending ethical, emotional, and clinical dimensions to enhance family engagement. Although FPDR offers significant benefits, such as fostering closure and transparency, addressing health care providers' concerns about potential disruptions and workflow challenges is essential to its effective and equitable implementation. OBJECTIVES: This article investigated critical care and emergency nurses' perceived risks and challenges of FPDR and barriers to implementing FPDR, as well as suggested measures to enhance the implementation of this care approach. METHODS: A qualitative descriptive approach was utilized using purposeful sampling to recruit critical care and emergency nurses from Midwestern US hospitals. Participants were interviewed using Zoom. RESULTS: Twenty-one nurses participated, predominantly female, White/non-Hispanic, with 1 to 25 years of experience and mostly bachelor-level education. The study identified 3 themes: risks and challenges of FPDR, barriers to its implementation, and recommendations for its facilitation. DISCUSSION: Participants highlighted barriers, challenges, and risks to FPDR, alongside facilitators such as institutional support, education, communication training, clear policies, family-related factors, assessment of family readiness, and designating a support person. Addressing these barriers and utilizing facilitators through education and strategic management can improve FPDR awareness and implementation in critical and emergency care.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".