Perceived Frequency and Importance of Elder Abuse Risk Factors in Arabic-Speaking Immigrant Communities
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".