Abuse of Marginalized Older Adults During COVID-19
Bibliographic record
Abstract
Abstract: Child and spousal abuse rates tend to increase during various disasters. This study sought to determine the prevalence and determinants of older adults’ experiences of increased verbal or physical conflict (+VPC) as a proxy for elder abuse during the COVID-19 pandemic. Data stem from the Canadian Longitudinal Study on Aging (CLSA), a prospective cohort study of 51,338 Canadians aged 45–85 at baseline. We analyzed the data of participants aged 55 or older at core follow-up 1 who also participated in a CLSA COVID-19 substudy ( n = 24,306). Experiencing +VPC was the main outcome variable; explanatory variables included gender identity, sexual orientation, age group, race/ethnicity, educational attainment, marital status, household income, working status, living arrangement (alone vs. with others), social support availability, cohesion in the community, self-rated health, anxiety, depression, and previous history of elder abuse. The overall weighted prevalence of +VPC was 7.4%. Gay/bisexual men, 55–64 age group, living with others, low social support, poor social cohesion, low self-rated health, poor mental health, and history of psychological or physical abuse were each significantly associated with +VPC. Weighted multivariable logistic regression revealed that male gender, living with others, higher depression and anxiety scores, and a history of psychological abuse were independent predictors of +VPC. Implications for postpandemic recovery and prevention strategies during future disasters include targeted outreach programs for the most vulnerable group, which included males and younger older adults between 55 and 64 years as well as those with mental health issues and/or history of elder psychological 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".