Exploring the Nature of Elder Abuse in Ethno-Cultural Minority Groups: A community-based participatory research study
Bibliographic record
Abstract
Elder abuse is a significant public health, social justice, and human rights issue in today’s society. Despite the recognition that elder1 abuse affects older adults across all racial, ethnic, and cultural groups, very little is known about the experiences of elder abuse among people from diverse ethno-cultural backgrounds in Canada. The primary objective of this study is to explore the nature of elder abuse within the two largest ethno-cultural minority groups in British Columbia (BC), the Chinese and South Asians (i.e., those who were either born in or can trace their ancestry to South Asia, which includes nations such as India, Pakistan, Sri Lanka, Bangladesh, and Nepal). Using a community-based participatory research approach,this study is a collaboration between three academics at the University of Victoria and four front-line workers from the Inter-Cultural Association of Greater Victoria (ICA), a not-for-profit, multicultural services organization for immigrants and refugees. The qualitative findings from this interview-based study reveal that cultural context, immigration status, and ethnicity are significant factors influencing experiences of elder abuse. Further, the findings provide insights into what resources — awareness and prevention — need to be developed in order to address the issue of elder abuse in these 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 | 0.033 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".