SELF-ADMINISTRATED ELDER ABUSE INTERVENTION FOR OLDER ADULTS WITH COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Many elder abuse interventions and tools designed to screen for abuse exclude older adults with cognitive impairments (CI) due to the challenges associated with screening and whether the older adult with CI can reliably report elder abuse. However, it has been shown that older adults with CI are among the most vulnerable to experiencing elder abuse. VOICES is an innovative, automated tablet-based elder abuse screening and prevention intervention that is self-administered by the older adult in the provider’s waiting room or office. The VOICES Elder Abuse Intervention (EAI) provides screening, educational modules, and brief psychoeducational intervention to enhance and improve identification of elder abuse when there are no visible signs of abuse. The VOICES EAI was already proven successful in terms of feasibility and acceptability in cognitively intact older adults in a busy emergency department setting with (N=1,002). In this study we tested the VOICES EAI with (N=30) participants 60 and above with cognitive impairment at a geriatric center using the Montreal Cognitive Assessment (MoCA) to determine cognitive capacity. Experts in the field of geriatrics and cognitive impairment assisted in grouping participants within three cognitive categories: Mild cognitive impairment (MoCA 23-25), mild dementia (MoCA 16-22) or moderate dementia (MoCA 8-15). Of the (N=30) participants, 29 were able to successfully use the VOICES EAI independently, and most participants were satisfied with the tool. We will discuss the findings of this preliminary study and the implications for future research with older adults with CI.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".