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Record W3117745931 · doi:10.1093/geroni/igaa057.2436

Evaluating an Integrated APS–Elder Advocate Intervention Model in the State of Maine

2020· article· en· W3117745931 on OpenAlexaff
David Burnes, Marie‐Therese Connolly, Patricia Kimball, Stuart Lewis, Erin Salvo

Bibliographic record

VenueInnovation in Aging · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIntervention (counseling)Motivational interviewingElder abusePsychologyDyadSocial workGoal Attainment ScalingNeglectBrief interventionNursingApplied psychologyMedicinePolitical scienceSocial psychologyMedical emergencyPoison controlSuicide prevention

Abstract

fetched live from OpenAlex

Abstract Despite recommendations to include a distinct intervention phase in Adult Protective Services (APS), most APS programs close cases following investigation/substantiation phases without engaging in a defined intervention phase. This study implements and evaluates a novel APS service planning/intervention model in the state of Maine. Using an experimental efficacy trial design with stratified random sampling at the level of Maine APS offices, this study compares standard APS care with an enhanced/integrated APS intervention model involving “elder advocates”. Advocates were trained in motivational interviewing, supported decision-making, teaming, restorative justice, and goal attainment scaling to develop capacity to work with both the older adult victim and perpetrator and to strengthen the family and social systems surrounding the victim-perpetrator dyad. This presentation will present results on the efficacy of this integrated APS/elder advocate model and discuss the challenges and successes in conducting elder abuse intervention research in collaboration with APS and APS clients. Part of a symposium sponsored by Abuse, Neglect and Exploitation of Elderly People Interest Group.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score0.320

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.133
GPT teacher head0.423
Teacher spread0.289 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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