Exploring the Linkages between Substance Use, Natural Disasters, Pandemics, and Intimate Partner Violence against Women: A Rapid Review in the Context of COVID-19
Bibliographic record
Abstract
Rates of intimate partner violence (IPV) and substance use have risen during the COVID-19 pandemic, with potentially enduring effects on women’s health. A rapid review was conducted on IPV and women’s substance use in the context of the COVID-19 pandemic. The rapid review explored two separate research questions with a view to integrate the literature related to: (1) containment, social isolation, pandemics, disasters, lockdowns, and IPV; and (2) the relationships between substance use and IPV. Two different searches for each question were conducted between May and October 2020 and n = 47 articles were included. Women experience multiple physical and mental health consequences related to IPV that can be exacerbated by public health crises such as pandemics and disasters. Perpetrators may use these events as a tactic to threaten, isolate, or use coercive control. Similar tactics are reported in the complex relationship between IPV and substance use, where substance use can accompany IPV and/or be used as a coping mechanism for survivors. The findings highlight long standing women’s health concerns made further visible during the COVID-19 pandemic. Additional research is needed to identify actions required to reduce gender inequities and harms associated with IPV and substance use, and to adequately tailor and prepare effective responses in the context of future public health crises.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".