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Record W3198639291 · doi:10.1017/s0714980821000295

Barriers to Help Seeking among Victims of Elder Abuse: A Scoping Review and Implications for Public Health Policy in Canada

2021· review· en· W3198639291 on OpenAlexaffabout
Jessica K. Gill

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2021
Typereview
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsElder abuseContext (archaeology)Intervention (counseling)Inclusion (mineral)Public healthExtant taxonPsychologyMedicineSuicide preventionPublic relationsPoison controlNursingCriminologyPolitical scienceEnvironmental healthSocial psychologyGeography

Abstract

fetched live from OpenAlex

Elder abuse is a serious public health concern requiring immediate intervention; however, the under-reporting of elder abuse by victims to formal and informal networks remains a major obstacle. This scoping review aims to identify barriers to help seeking that older adults experiencing abuse confront. The goal is to inform public policies and practices in the Canadian context and identify research gaps in the extant literature. Seven scholarly databases were searched from which 12 articles met the inclusion criteria and were extracted for analysis. The findings from this scoping review revealed three levels at which barriers exist: individual focused, abuser/family focused, and community/culture focused barriers. The results suggest that there are several complex obstacles that older adults face when contemplating disclosure of abuse. Future research into help seeking in the Canadian context should more readily incorporate the voices of elder abuse victim-survivors to develop effective assessment strategies and responsive service provisions.

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)0.016
metaresearch head score (Gemma)0.056
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.180
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.056
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0170.030
Science and technology studies0.0040.002
Scholarly communication0.0070.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.331
Teacher spread0.293 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations17
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicElder Abuse and NeglectFrench-language works237,207