Elder abuse risk factors: Perceptions among older Chinese, Korean, Punjabi, and Tamil immigrants in Toronto
Bibliographic record
Abstract
OBJECTIVES: Elder abuse is a significant concern worldwide. Several factors are reported to increase the risk for elder abuse, but little is known about which factors are most relevant to immigrant communities. This study explored perceptions of risk factors for elder abuse among older immigrants, which is the first step toward designing effective interventions. METHODS: = 173) of older women and men from Chinese, Korean, Punjabi, and Tamil immigrant communities. Participants completed a questionnaire about the frequency and importance of risk factors of elder abuse in their respective community. Descriptive statistics were used to analyze the data within each immigrant community and analysis of variance to compare the factor ratings across communities. RESULTS: < .05) in their perception of the risk factors. Factors rated as frequent and important (x̅ > 2.0 - midpoint of the rating scale) were social isolation, financial dependence, and lack of knowledge of English for Korean; financial dependence, physical dependence, and emotional dependence for Chinese; lack of knowledge of English, emotional dependence, and physical dependence for Tamil; and social isolation for Punjabi. CONCLUSION: The findings highlight the need for collaboration among public health and social services to work with immigrant communities in co-designing interventions to address these key risk factors and thereby reduce the risk of elder abuse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".