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Record W3113576314 · doi:10.1093/swr/svaa018

Mental Health Consequences and Service Use of Older Adults at Risk of Financial Exploitation

2020· article· en· W3113576314 on OpenAlexaff
Angela Lavery, Leslie Hasche, Anne P. DePrince, Kerry L. Gagnon, Tejaswinhi Srinivas, Erin Boyce

Bibliographic record

VenueSocial Work Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMental healthDepression (economics)Psychological interventionFinancePsychologyPsychiatryElder abuseMedicineSuicide preventionGerontologyPoison controlMedical emergencyBusiness

Abstract

fetched live from OpenAlex

Abstract This study examines whether the experience of financial exploitation is associated with increased likelihood of mental health consequences and subsequent use of mental health services. Interviews were conducted with 99 participants, age 60 and older, at risk for elder abuse. The authors and research team administered standardized measures of elder mistreatment, depression, trauma, social support, and service use. Older adults who experienced financial exploitation reported worse trauma symptoms and depression than those who did not experience financial exploitation. Those with increased functional impairment were least likely to rely on mental health services. The findings highlight the importance of interventions to address and enhance response to depression, trauma, and social support in older adults who are victims of financial exploitation.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.140
GPT teacher head0.410
Teacher spread0.270 · 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 designObservational
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

Citations15
Published2020
Admission routes1
Has abstractyes

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