Virtual Patient and Family Engagement Strategies in Critical Care: A Scoping Review
Bibliographic record
Abstract
Background: Family engagement in care is increasingly recognized as an essential component of optimal critical care delivery. However, family engagement strategies have traditionally involved in-person family participation. Virtual approaches to family engagement may overcome barriers to family participation in care. The objective of this study was to perform a scoping review of virtual family engagement strategies in the intensive care unit (ICU). Methods: Studies were included if they involved a virtual engagement strategy with family members of an ICU patient and reported either (1) outcomes, (2) user perspectives, and/or (3) barriers or facilitators to virtual engagement in the ICU. Study types included primary research studies and review articles. Study selection followed the Joanna Briggs Institute Methodology for Scoping Reviews guidelines without any cultural, ethnic, gender, or specific language restrictions. The source of evidence included Ovid MEDLINE, PubMed, CINAHL, and Cochrane Library databases from inception to November 17, 2023. Google scholar was searched on December 1, 2023. Data were extracted on virtual engagement strategy used, outcomes (patient-centered, family-centered, and clinical), perspectives (patient, family, and health care professional [HCP]), and reported barriers or facilitators to virtual engagement in the ICU. Results were categorized into adult or pediatric/neonatal ICU setting. Results: Virtual engagement strategies identified were virtual visitation, virtual rounding, and virtual meetings. Family and HCPs were generally supportive of virtual visitation and rounding strategies. Overall, virtual strategies were associated with improved patient, family, and HCP outcomes. There were a few randomized interventional studies evaluating the effectiveness of virtual engagement strategies. Family, HCP, technological, and institutional barriers to the implementation and conduct of virtual engagement strategies were reported. Conclusions: Virtual family engagement strategies are associated with improved outcomes for patients, family, and HCPs. Identified barriers to virtual family engagement should be addressed. Future studies are needed to evaluate the effectiveness of virtual family engagement strategies in a more rigorous manner.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 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".