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Record W4390198669 · doi:10.1002/alz.082224

Digital Elder Abuse Intervention for Older People Living with Dementia

2023· article· en· W4390198669 on OpenAlexaboutno aff
Fuad Abujarad, Chelsea Edwards, Judith Neugroschl, Ula Hwang, Richard A. Marottoli

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsDementiaMontreal Cognitive AssessmentPsychological interventionIntervention (counseling)MedicineCognitionGerontologyPsychologyClinical psychologyPsychiatryCognitive impairmentDisease

Abstract

fetched live from OpenAlex

Abstract Background A growing number of older adults experience elder abuse (EA) each year, and people living with dementia (PLWD) are at higher risk. Emergency departments (EDs) are opportune settings to identify EA and can provide interventions to those at high risk and direct them to appropriate health and social services. There is evidence that PLWD can report abuse despite cognitive challenges, but research is limited regarding self‐administered interventions to detect EA. Method We developed and tested our tablet‐based intervention to increase self‐reporting of EA among PLWD. The VOICES EA intervention combines educational resources, screening, multimedia elements, and brief psychoeducational interviewing to motivate PLWD to self‐identify and self‐report abuse. The goal of our study is to evaluate using VOICES among PLWD (ages 60+ years) at a geriatric memory clinic (N = 30) and the ED setting (N = 101). We previously demonstrated VOICES’ feasibility and efficacy with 1,000 cognitively intact participants (ages 60+) in the ED and wanted to compare patterns of use between users (with or without dementia). Participants were recruited, consented, and enrolled at Yale New Haven Health System in New Haven, Connecticut, USA. We used the Montreal Cognitive Assessment (MoCA) to assess cognition of participants, excluding participants with severe dementia. Result Older adults with MoCA scores between 14 and 25 were able to use VOICES similarly to those without cognitive impairments (i.e., MoCA >25) and were able to complete using the VOICES independently. From the memory clinic, 83% agreed that audio and visual tool aids were helpful, and 87% agreed that the tool’s instructions were easy to follow. In the ED setting, of the 101 participants who started VOICES, 100 completed VOICES independently (99% completion rate). Of participants who completed the post‐use questions (N = 99), 84% were satisfied with their ability to complete VOICES on their own without assistance, and 91% agreed VOICES was appropriate for learning about EA. Conclusion Our results suggest that the VOICES EA intervention is feasible (i.e., acceptable, practical, and satisfactory) for detecting EA in PLWD, specifically those with MoCA scores ranging from 14‐25. This may allow for enhanced early EA detection and intervention in this highly vulnerable population.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.024
GPT teacher head0.295
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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