Developing a comprehensive understanding of elder abuse prevention in immigrant communities: a comparative mixed methods study protocol
Bibliographic record
Abstract
INTRODUCTION: Older adults are the fastest growing age group in Canada. Elder abuse has significant individual and societal implications, so it is critical to address. While interest in this topic is increasing, little is known about the risk factors for elder abuse in immigrant communities in Canada, or about culturally relevant strategies to address these risk factors. METHODS AND ANALYSIS: This mixed-methods study is guided by the intersectionality and ecological frameworks. We will include two long-term (ie, established) and two recent immigrant communities from East Asian and South Asian communities in the Greater Toronto Area: Chinese, Korean, Punjabi and Tamil. Through structured group interviews, we will first identify factors that contribute to elder abuse within and across each of the immigrant communities and then explore culturally relevant strategies to address those risk factors. Group interviews will be conducted separately with five stakeholder groups in each of the four languages: older women, older men, family members, community leaders and service providers. Quantitative and qualitative data will be analysed at the level of the particular interview groups, subgroups and communities, and will be integrated across communities to identify common and unique risk factors and strategies to address elder abuse. ETHICS AND DISSEMINATION: The study protocol has received ethics approval from the two universities associated with the research team. Given the comprehensive approach to incorporate local knowledge and expert contributions from multi-level stakeholders, the empirical and theoretical findings will facilitate practice change and improve the well-being of older men and women in 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 | 0.109 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.042 | 0.006 |
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".