A Digital Tool to Improve Family Engagement in Acute Care: The NGAGE Randomized Pilot Feasibility Trial
Bibliographic record
Abstract
Background Engaging families in care is an important aspect of healthcare delivery. Our interdisciplinary team designed the NGAGE tool to empower family members to increase their engagement in patient care. The objective of this study was to assess the feasibility of the NGAGE tool. Methods We conducted a single-center pilot feasibility randomized controlled trial in the acute cardiac unit of a tertiary care academic hospital. Family members of admitted patients were randomized in a 1:1 manner to the intervention (use of the NGAGE tool) or usual care. Intervention group participants could request engagement activities using the NGAGE tool and this request was transmitted to the treating healthcare team. At enrolment, demographics and family engagement scores were captured. At discharge, care satisfaction, mental health, and engagement scores were collected. Recruitment rate, intervention uptake, tool use, requests completed, and follow-up rate were used to asses feasibility outcomes. Results There were 88 participants (45 intervention, 43 control) in the analysis. The mean recruitment rate was 2.9 participants/week. Two-thirds (69%) of participants in the intervention group used the NGAGE tool (1.5±0.9 uses/participant). Most engagement requests (39/47; 83.0%) were completed. Follow-up data was available for three-quarters of participants (67/88; 76%). Family members who used the NGAGE tool improved their family engagement score. There were no between group differences in mental health or satisfaction at follow-up (all p>0.05). Conclusions We found evidence to suggest that the NGAGE tool was feasible. The results will inform the design of a multi-center effectiveness trial of the NGAGE tool.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".