PREVALENCE AND PREDICTORS OF INCREASED VERBAL/PHYSICAL CONFLICT DURING COVID-19: CLSA FINDINGS
Bibliographic record
Abstract
Abstract Child and spousal abuse rates have been shown to increase during various types of 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 are from the Canadian Longitudinal Study on Aging (CLSA), a cohort study of 51,338 Canadians aged 45–85 at enrollment (2012-15) with follow-up every 3 years until 2033.. We analyzed data of participants aged 65 or above at follow-up1 who took part in a COVID-19 sub-study (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 alone, social support availability, cohesion with community, self-rated physical and mental 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, not living alone, low social support, poor social cohesion, low self-rated health, poor mental health, and past history of psychological or physical abuse were all significantly associated with +VPC. Weighted multivariable logistic regression revealed gender, not living alone, higher scores of depression and anxiety, and past history of psychological abuse to be independent predictors of +VPC. Implications for post-pandemic recovery and for prevention strategies during future disasters include targeted outreach programs for the most vulnerable group which includes males, persons age 55-64, those with self-rated poor mental health 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".