Concerns about Household Violence during the COVID-19 Pandemic
Bibliographic record
Abstract
Evidence about how the pandemic affected household violence in Canada is mixed, but inarguably, the risk factors increased. This study used data from the 2020 Canadian Perspective Survey Series and the 2020 and 2021 Surveys of COVID-19 and Mental Health to examine the following: changes in the prevalence of concern about violence in individuals' own homes during the pandemic; the characteristics of those who expressed concern; and the prevalence of concerns for specific household members. Among Canadians, the prevalence of concern about violence in individuals' own homes decreased significantly between July and Fall 2020 (5.8% to 4.2%). Among women, the characteristics that were significantly associated with higher adjusted odds of concern about household violence included larger household size and lower household income. Lower education among women was associated with lower adjusted odds of concern. The associations with higher adjusted odds of concern among men included: being an immigrant, larger household size, and lower household income. From Fall 2020 to Spring 2021, the prevalence of concerns for oneself and for a child/children increased (1.7% to 2.5% and 1.0% to 2.5%, respectively), but concern for other adults in the household decreased (1.9% to 1.2%). Ongoing surveillance is needed to understand vulnerable populations' exposure to household violence and to inform policies and programs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".