Addressing the “shadow pandemic” through a public health approach to violence prevention
Bibliographic record
Abstract
Experts from across the globe have warned of the adverse consequences of COVID-19 lockdown and physical distancing restrictions on violence in the home, with the United Nations describing it as a shadow pandemic. This social innovation narra-tive explores how a public health approach to violence prevention is implemented in Wales during the COVID-19 pandemic by the multi-agency Wales Violence Prevention Unit. The article highlights early trends in monitoring data on the impact of COVID-19 restrictions on violence, including likely increases in domestic and sexual violence and abuse, concerns over the safety of children and young people, both online and in the home, and increased reporting of elder abuse. The article supports the notion of a shadow pandemic, emphasizing the lack of data that routinely measures violence in the home and online that disproportionately affects women, children, and older people, as well as vulnerable and minority populations. This renders these forms of violence much less “visible” to policy-makers in comparison with violence in public spaces, but they are of no less public health significance. Through sharing this narrative and early findings, we call for increased focus on the develop-ment of new data collection methods and violence prevention programs during the COVID-19 pandemic and in the future.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.010 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".