The Effects of the COVID-19 Pandemic on Family Violence: A Meta-Analytical Investigation
Bibliographic record
Abstract
The association between family violence and the COVID-19 pandemic remains complex. This meta-analysis aimed to determine trends in the observed changes in family violence comparing the pre-pandemic period to the pandemic period. A systematic search was performed in electronic databases to identify all relevant research reporting on COVID-19 and family violence. There was a statistically significant increase in family violence after the first lockdown. The odds ratio for the prevalence of physical and sexual violence together was 7.24 (95% CI = 4.74, 11.03 p < 0.001). A small marginal increase in the prevalence of various types of family violence leading to hospitalization was found, however, the result was not statistically significant (OR = 1.91, 95% CI = 0.91, 3.96, p = 0.09). A small significant increase in the prevalence of victims with a perception of increased violence during the pandemic lockdown was observed (proportion = 33%, 95% CI = 15.72%, 50.34%, p = 0.002). This meta-analysis found that during the COVID-19 lockdown, there was an increase in the prevalence of overall family violence, a small, non-significant, increase in the prevalence of hospitalizations due to family violence, as well as an increase in the perception of family violence by victims. These results are clinically relevant for implementing effective measures of violence prevention to safeguard vulnerable populations.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.049 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".