The Impact of Randomized Family-Centered Interventions on Family-Centered Outcomes in the Adult Intensive Care Unit: A Systematic Review
Bibliographic record
Abstract
Objective: To review the literature for randomized family-centered interventions with family-centered outcomes in the adult intensive care unit (ICU). Data Sources: We searched MEDLINE, EMBASE, PsycINFO, CINAHL, and the Cochrane Library database from inception until February 2023. Study Selection: We included articles involving randomized controlled trials (RCTs) in the adult critical care setting evaluating family-centered interventions and reporting family-centered outcomes. Data Extraction: We extracted data on author, year of publication, setting, number of participants, intervention category, intervention, and family-centered outcomes. Data Synthesis: There were 52 RCTs included in the analysis, mostly involving communication and receiving information (38%) and receiving care and meeting family member needs (38%). Nearly two-thirds of studies (N = 35; 67.3%) found improvements in at least 1 family-centered outcome. Most studies (N = 24/40; 60%) exploring the impact of family-centered interventions on mental health outcomes showed improvement. Improvements in patient-centered outcomes (N = 7/17; 41%) and healthcare worker outcomes (N = 1/5; 20%) were less commonly found. Conclusions: Family-centered interventions improve family-centered outcomes in the adult ICU and may be beneficial to patients and healthcare workers.
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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 | 0.021 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".