Global and Regional Prevalence of Domestic Violence During the COVID-19 Pandemic and Its Determinants: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Domestic violence is one of the most significant global public health priorities. This social problem could be accelerated by global catastrophes such as the COVID-19 pandemic. The structural changes due to the imposition of health measures, combined with personal and social problems, may worsen the situation. OBJECTIVE: This study aims to investigate the global and regional prevalence of domestic violence during the COVID-19 pandemic and its determinants. METHODS: We will perform a comprehensive review of the literature in PubMed, PsycINFO, Embase, Cochrane COVID-19 Register, and Applied Social Sciences Index and Abstracts, up to July 2024. This review will adhere to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting guidelines. Observational studies will be considered eligible if they have a population-based design, report the number of cases or prevalence of domestic violence during the COVID-19 pandemic, and report potential determinants. Studies in languages other than English, those with unclear data, case reports, conference proceedings, reviews, and letters will be excluded. To assess the methodological quality, a standardized critical appraisal checklist for studies reporting prevalence data will be used. A robust Bayesian approach will be applied using the STATA software package (version 14; STATA Inc) and JASP 0.19.1 (GNU Affero General Public License [GNU AGPL]) software. RESULTS: The search and screening for the systematic literature review are anticipated to be finished in October 2024. Data extraction, quality appraisal, and subsequent data synthesis will begin in November 2024. The review is expected to be completed by April 2025, and the study results will be published in 2025. CONCLUSIONS: This systematic review and meta-analysis will address significant gaps in understanding the pandemic's impact on domestic violence, providing a comprehensive assessment of its prevalence and contributing factors. Despite some limitations, the study incorporates diverse data sources and vulnerable groups to offer a detailed and accurate picture. The findings will inform targeted interventions and policy responses to mitigate the impact of future global crises on domestic violence rates. TRIAL REGISTRATION: PROSPERO CRD42022351634; https://tinyurl.com/yth37jkx. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60963.
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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 | 0.088 | 0.120 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.010 |
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".