What measures have been used to explore the outcomes of family members of critically ill patients: a scoping review
Bibliographic record
Abstract
BACKGROUND: Family members of critically ill patients are known to experience short and longer term challenges, including emotional and social problems. To undertake robust future interventional research to support this cohort, we undertook a comprehensive evaluation of the measures used in family outcome research to date. DESIGN: Scoping review. We searched Medline, PsycINFO, Scopus, CINAHL, and Web of Science (core collection) for articles published between 2000 and 2024 which examined the outcomes of family members of critically ill patients. Data on outcome measures, study design, and population characteristics were extracted from eligible studies. RESULTS: We reviewed 9873 abstracts and identified 388 eligible unique family member cohorts. Across these studies, there were 59 (15.2%) randomised control trials, 287 (74.0%) observational cohorts, 8 (2.1%) quality improvement programmes, and 34 (8.8%) non-randomised controlled interventional studies. Most research was undertaken in USA, Canada, and Europe (n = 264, 67.5%). A total of 218 different outcomes measures were used across the studies included. In this review, 345 (89%) studies measured emotional or psychological outcomes, 85 (22%) measured family experience outcomes, 65 (17%) measured general functional status, quality of life or well-being outcomes, 35 (9%) measured environmental or social outcomes, and 10 (3%) measured cognitive outcomes. CONCLUSIONS: We identified 388 studies which reported the outcomes of family members of critically ill patients. Over 200 different outcomes measures were utilised, exploring conceptual domains such as quality of life as well as psychological and social outcomes. The majority of studies were observational in nature with variable follow-up timelines. This review has highlighted that a core outcome measure set is essential to enable reliable, robust, and comparable interventional research in this area.
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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 | 0.039 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.031 | 0.029 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".