Determinants of Family Stress and \nDomestic Violence: Lessons from the \nCOVID-19 Outbreak
Bibliographic record
Abstract
Dans cet article, nous nous intéressons à l'impact de la maladie à coronavirus 2019 (COVID-19) sur la violence familiale et le stress familial. Notre analyse empirique repose sur une enquête unique menée en ligne, la Série d'enquêtes sur les perspectives canadiennes (SEPC) qui nous permet d'étudier les facteurs de préoccupation quant au stress et à la violence au sein de la famille au cours du premier confinement du COVID-19. Nous n'avons pas constaté que les changements apportés aux modalités de travail avaient une incidence sur la violence ou le stress au sein de la famille. Au contraire, nous avons trouvé que l'incapacité d'assumer les obligations financières et le souci de maintenir des liens sociaux étaient reliés de manière significative aux préoccupations quant au stress familial et à la violence familiale. Abstract: In this article, we examine Canadians' concerns regarding the impact of coronavirus disease 2019 (COVID-19) on domestic violence and family stress. Our empirical analysis relies on a unique survey conducted online, the Canadian Perspective Survey Series, which allows us to investigate the determinants of concerns regarding family stress and domestic violence during the first COVID-19 lockdown. We find no evidence that changes in work arrangements are related to concerns regarding family stress and violence in the home. In contrast, we find that the inability to meet financial obligations and concerns about maintaining socialties are significantly related to concerns about family stress and domestic violence.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".