FEASIBILITY OF A DIGITAL ELDER MISTREATMENT INTERVENTION FOR COGNITIVELY IMPAIRED OLDER ADULTS
Bibliographic record
Abstract
Abstract Older adults with cognitive impairments (CI) are at a higher risk for experiencing elder mistreatment (EM) and are less likely to report. Research is limited for self-administrated digital screening in this population, and generally focus on provider-based solutions to increasing EM identification. We developed a digital health intervention, VOICES, to increase EM identification. VOICES combines educational resources, screening, and brief psychoeducational interviewing to encourage older adults to self-report. We recruited participants with CI at a geriatric outpatient center (N=30) and the emergency department (ED) (N=101) to examine feasibility. Most participants could use VOICES independently (Montreal Cognitive Assessment (MoCA) scores ranging from 14-25). 87% of ED participants felt VOICES was appropriate for use in the ED, and 97% felt that the digital coach instructions were easy to understand. Our results suggest that the VOICES is feasible for EM screening in older adults with CI, specifically with MoCA scores ranging from 14-25.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".