A Web-Based Platform (CareVirtue) to Support Caregivers of People Living With Alzheimer Disease and Related Dementias: Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: People living with Alzheimer disease and related dementias (ADRD) require prolonged and complex care that is primarily managed by informal caregivers who face significant unmet needs regarding support for communicating and coordinating across their informal care network. To address this unmet need, we developed CareVirtue, which provides (1) the ability to invite care network members; (2) a care guide detailing the care plan; (3) a journal where care network members can document, communicate, and coordinate; (4) a shared calendar; and (5) vetted geolocated caregiver resources. OBJECTIVE: This study aims to evaluate CareVirtue's feasibility based on: (1) Who used CareVirtue? (2) How did caregivers use CareVirtue? (3) How did caregivers perceive the acceptability of CareVirtue? (4) What factors were associated with CareVirtue use? METHODS: We conducted a feasibility study with 51 care networks over a period of 8 weeks and used a mixed methods approach that included both quantitative CareVirtue usage data and semistructured interviews. RESULTS: Care networks ranged from 1 to 8 members. Primary caregivers were predominantly female (38/51, 75%), White (44/51, 86%), married (37/51, 73%), college educated (36/51, 71%), and were, on average, 60.3 (SD 9.8) years of age, with 18% (9/51) living in a rural area. CareVirtue usage varied along 2 axes (total usage and type of usage), with heterogeneity in how the most engaged care networks interacted with CareVirtue. Interviews identified a range of ways CareVirtue was useful, including practically, organizationally, and emotionally. On the Behavioral Intention Scale, 72% (26/36) of primary caregivers reported an average score of at least 3, indicating an above average intention to use. The average was 81.8 (SD 12.8) for the System Usability Scale score, indicating "good" usability, and 3.4 (SD 1.0) for perceived usefulness, suggesting above average usefulness. The average confidence score increased significantly over the study duration from 7.8 in week 2 to 8.9 in week 7 (P=.005; r=0.91, 95% CI 0.84-0.95). The following sociodemographic characteristics were associated with posting in the journal: retired (mean 59.5 posts for retired caregivers and mean 16.9 for nonretired caregivers), income (mean 13 posts for those reporting >US $100K and mean 55.4 for those reporting
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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 | 0.042 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".