Measuring family engagement in intensive care: Validation of the FAME tool
Bibliographic record
Abstract
INTRODUCTION: Engaging family members in patient care in the intensive care unit (ICU) is a recommended practice by critical care societies. However, there are currently no validated tools to measure family engagement in the ICU setting. The objective of this study was to validate the FAMily Engagement (FAME) tool in the ICU. METHODS: The FAME study was a multicenter prospective cohort study of family members of ICU patients in 8 Canadian ICUs. Family members completed the FAME questionnaire during the ICU stay. The FAME questionnaire comprised 12 items that assessed various domains of family engagement behavior. FAME scores were reported in a 0-100 scoring system with higher scores indicating increased care engagement. Following hospital discharge, we assessed associations between the FAME score and family satisfaction with care and mental health (anxiety and depression). The internal consistency (reliability), convergent validity, and predictive validity of the FAME tool were evaluated. RESULTS: There were 269 family members (age 56.8 ± 15.1; 68.4 % women) included in the analysis. The most common relationships to the patient were spouse/partner (40.5 %) and daughter/son (33.8 %). The overall mean FAME score was 77.7 ± 14.8. The FAME score had high internal consistency (Cronbach's α = 0.83) and the tool demonstrated convergent and predictive validity. The FAME score was associated with family satisfaction, but not with mental health outcomes. CONCLUSION: The FAME tool demonstrated reliability, convergent, and predictive validity in this multicenter ICU cohort. The FAME tool could be used to evaluate the effectiveness of family engagement interventions.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".