Older Adults and Family Discord or Violence During the COVID-19 Pandemic: Results of a Canada-Wide Survey
Bibliographic record
Abstract
Abstract Child abuse and intimate partner violence rates are known to increase during and in the aftermath of disasters. Research on elder abuse during disasters, including the current pandemic, is limited. As part of an online survey that explored older Canadians’ current experiences and future care plans during the COVID-19 pandemic, we aimed to determine the prevalence, contributing factors and potential outcomes of frequent family discord involving physical violence (FFD/PV) as a proxy for elder abuse. The survey was conducted between Aug 10 and Oct 10, 2021. Respondents (n=4380) were recruited using social media, direct email, Facebook advertising and with the assistance of 85 local community, regional and national organizations. The sub-sample reporting FFD/PV (n=76, 1.8%) was compared with other survey respondents regarding socio-demographic characteristics, negative and positive emotions, difficulty accessing basic needs, food, health care and support. Respondents experiencing FFD/PV were found to be significantly younger and less educated and were more likely to be non-white and not working than other respondents. The subgroup sustaining FFD/PV reported significantly higher rates of feeling depressed, lonely, isolated, anxious, sad, and judged/shamed and felt less happy, relaxed and accepted in their community. They also reported higher rates of challenges in accessing basic material needs such as food, support, medical care, mental health treatment and experienced more changes in life routines. Although only a small percentage reported FFD/PV, our results highlight a disturbing pattern that merits serious attention of adult protection agencies, seniors' advocates and disaster response organizations.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".