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Record W4285137706 · doi:10.4236/aar.2022.113006

Perceived Frequency and Importance of Elder Abuse Risk Factors in Arabic-Speaking Immigrant Communities

2022· article· en· W4285137706 on OpenAlexaffabout
Sepali Guruge, Ernest Leung, Souhail Boutmira, Souraya Sidani

Bibliographic record

VenueAdvances in Aging Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsElder abuseSocial isolationEthnic groupArabicImmigrationOutreachLanguage barrierPsychologyPerceptionMedicineGerontologySuicide preventionPoison controlPsychiatryPolitical scienceEnvironmental healthLinguistics

Abstract

fetched live from OpenAlex

Background: Although the number of older immigrants and the prevalence of elder abuse are increasing in Canada, little is known about their experience of risk factors for elder abuse. This study examined Arabic-speaking older immigrants’ perception of the factors that increase the risk for elder abuse. Methods: Older Arabic-speaking women (n = 24) and men (n = 31) completed a questionnaire that inquired about the perceived frequency and importance of factors that contribute to elder abuse. Descriptive statistics were used to analyze the data. Results: Older women identified lack of English language proficiency, social isolation, and financial dependence as the most frequent, and lack of English language proficiency, income, and sponsorship status as the most important risk factors. Older men rated social isolation, lack of English language proficiency, and financial dependence as the most frequent, and social isolation, racialized, cultural or ethnic group status, and lack of English language proficiency as the most important factors contributing to elder abuse. Conclusion: Offering language-specific services, designing tailored outreach programs to address social isolation, and addressing systemic barriers that create financial dependence can help prevent elder abuse in Arabic-speaking immigrant communities.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.390
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.395
Teacher spread0.341 · 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
Published2022
Admission routes2
Has abstractyes

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