Digital or Digitally Delivered Responses to Domestic and Intimate Partner Violence During COVID-19
Bibliographic record
Abstract
Before the coronavirus disease (COVID-19), 1 in 3 women and girls, globally, were victimized by an abusive partner in intimate relationships. However, the current pandemic has amplified cases of domestic violence (DV) against women and girls, with up to thrice the prevalence in DV cases compared to the same time last year. Evidence of the adverse effects of the pandemic on DV is still emerging, even as violence prevention strategies are iteratively being refined by service providers, advocacy agencies, and survivors to meet stay-at-home mandates. Emotional and material support for survivors is a critical resource increasingly delivered using digital and technology-based modalities, which offer several advantages and challenges. This paper rapidly describes current DV mitigation approaches using digital solutions, signaling emerging best practices to support survivors, their children, and abusers during stay-at-home advisories. Some examples of technology-based strategies and solutions are presented. An immediate priority is mapping out current digital solutions in response to COVID-19-related DV and outlining issues with uptake, coverage, and meaningful use of digital solutions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".