Development and initial validation of a family activation measure for acute care
Bibliographic record
Abstract
BACKGROUND: Activation of a family member refers to their desire, knowledge, confidence, and skills that can inform engagement in healthcare. Family activation combined with opportunity can lead to engagement in care. No tool currently exists to measure family activation in acute care. Therefore, we aimed to develop and validate a tool to measure family activation in acute care. METHODS: An interdisciplinary team of content experts developed the FAMily Activation Measure (FAM-Activate) through an iterative process. The FAM-Activate tool is a 4-item questionnaire with 5 Likert-type response options (ranging from strongly agree to strongly disagree). Scale scores are converted to a 0-100 point scoring range so that higher FAM-Activate scores indicate increased family activation. An overall FAM-Activate score (range 0-100) is calculated by adding the scores for each item and dividing by 4. We conducted reliability and predictive validity assessments to validate the instrument by administering the FAM-Activate tool to family members of patients in an acute cardiac unit at a tertiary care hospital. We obtained preliminary estimates of family engagement and satisfaction with care. RESULTS: We surveyed 124 family participants (age 54.1±14.4; 73% women; 34% non-white). Participants were predominantly the adult child (38%) or spouse/partner (36%) of patients. The mean FAM-Activate score during hospitalization was 84.1±16.1. FAM-Activate had acceptable internal consistency (Cronbach's a = 0.74) and showed test-retest responsiveness. FAM-Activate was moderately correlated with engagement behavior (Pearson's correlation r = 0.47, P <0.0001). The FAM-Activate score was an independent predictor of family satisfaction, after adjusting for age, gender, relationship, and living status. CONCLUSION: The FAM-Activate tool was reliable and had predictive validity in the acute cardiac population. Further research is needed to explore whether improving family activation can lead to improved family engagement in 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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".