Arabic-speaking older immigrants’ perceived acceptability of interventions for preventing elder abuse
Bibliographic record
Abstract
Objective: Although research has identified interventions to address risk factors for elder abuse, it is unclear which interventions are relevant to specific immigrant communities. This study examined how Arabic-speaking immigrants in the Greater Toronto Area perceived the acceptability of interventions for elder abuse and explored gender differences in these perceptions. Methods: Older women and men (N = 37) who self-identify as Arabic-speaking immigrants residing in the Greater Toronto Area rated the acceptability of 14 interventions. The literature describes these interventions as addressing the risk factors for elder abuse as reported at the levels of older adults, the family, their relationship, and the social environment. Four items, adapted from a validated measure, were used to assess the interventions’ acceptability. The data were analyzed using descriptive statistics (objective 1) and independent sample t-test (objective 2). Results: Arabic-speaking older immigrants perceived five interventions to prevent elder abuse in their community as highly acceptable: case management, community outreach, advocacy, community-outreach programs, and peer-support programs. Gender differences were found for four interventions: two interventions (case management and community outreach) targeted older adults, one intervention (education) targeted the family, and one (advocacy) focused on the social environment. Conclusion: Findings can inform service providers, managers, and policymakers about which interventions must be prioritized to address elder abuse in the Arabic-speaking immigrant community.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".