Violence Across the Lifespan: Interconnections Among Forms of Abuse as Described by Marginalized Canadian Elders and their Care-givers
Bibliographic record
Abstract
Elder abuse is recognized as a major problem, with profound effects on the health and quality of life of older persons. In our aging population, elder abuse represents an escalating clinical issue for social workers and health care professionals who provide care to older people. A major gap in our examination of elder abuse is the potential contribution and application of knowledge developed within research derived from other forms of family violence. This paper explores the interconnections among various forms of violence across the lifespan, and the experiences voiced by marginalized elders and their care providers. We interviewed seventy-seven rarely consulted older adults and forty-three formal and informal care-givers of older adults in focus groups in Ontario and Alberta, Canada. Study findings revealed four major themes that describe interconnections among types of abuse: (i) intergenerational cycles of abuse; (ii) violence across the lifespan; (iii) exposure to multiple subtypes of elder abuse; and (iv) ongoing spouse abuse that shifted into elder abuse. The results from this study indicate that victims often ‘suffer in silence’ and cultural factors, ageism and gender are ubiquitous to elder abuse. Recommendations to reduce elder abuse include education, formal and informal supports and services.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".