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Record W2900144687 · doi:10.1093/geroni/igy023.530

INFORMAL NETWORK SUPPORTERS MAKE A DIFFERENCE IN FACILITATING USE OF FORMAL SUPPORT SERVICES FOR ELDER ABUSE VICTIMS

2018· article· en· W2900144687 on OpenAlexaff
David Burnes, Risa Breckman, C. Henderson, Mark S. Lachs, Karl Pillemer

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElder abuseEnforcementSocial supportPopulationLaw enforcementService (business)PsychologyMedicineCriminologySocial psychologySuicide preventionPoison controlMedical emergencyBusinessPolitical scienceLawEnvironmental health

Abstract

fetched live from OpenAlex

The hidden nature of elder abuse remains a major challenge in the field. Few elder abuse victims ever seek or receive assistance from formal support services (e.g., adult protective services, law enforcement) designed to ameliorate the effects of abuse and prevent re-victimization. This study examined whether the presence of a third-party “concerned person” in a victim’s social support network plays a role in enabling formal support service utilization. A representative population-based survey administered to a random sample of adults (n = 800) in New York State identified 83 cases of elder abuse from the past year. Penalized likelihood logistic regression was used to examine the relationship between availability of a concerned person and victim use of formal support services. Elder abuse victims who had a concerned person in their personal network were significantly more likely to use formal elder abuse support services than victims without a concerned person. Additionally, elder abuse victims who lived with their perpetrator were significantly less likely to use formal support services. Third-party concerned persons in a victim’s social support network represent a critical population to target in prevention efforts designed to promote elder abuse victim help-seeking and participation in the formal support system.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.037
GPT teacher head0.319
Teacher spread0.282 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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