2020 Syndemic: Convergence of COVID-19, Gender-Based Violence, and Racism Pandemics
Bibliographic record
Abstract
OBJECTIVE: To conduct a rapid knowledge synthesis of literature on the social determinants of mental health of racialized women exposed to gender-based violence (GBV) during the COVID-19 pandemic. METHODS: We adapted the Cochrane Rapid Reviews method and were guided by an equity lens in conducting rapid reviews on public health issues. Four electronic databases (Cochrane CENTRAL, Medline, ProQuest, and EBSCO), electronic news media, Google Scholar, and policy documents were searched for literature between January 2019 and October 2020 with no limitations for location. Fifty-five articles qualified for the review. RESULTS: Health emergencies heighten gender inequalities in relation to income, employment, job security, and working conditions. Household stress and pandemic-related restrictions (social distancing, closure of services) increase women's vulnerability to violence. Systemic racism and discrimination intensify health disparities. CONCLUSION: Racialized women are experiencing a 2020 Syndemic: a convergence of COVID-19, GBV, and racism pandemics, placing their wellbeing at a disproportionate risk. GBV is a public health issue and gender-responsive COVID-19 programming is essential. Anti-racist and equity-promoting policies to GBV service provision and disaggregated data collection are required.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".