Intersecting inequities: a scoping review of the gendered relationship between unpaid care work and intimate partner violence during the COVID-19 lockdown in Canada
Bibliographic record
Abstract
BACKGROUND: While there is now extensive research on how COVID-19 lockdowns negatively affected unpaid care burdens and intimate partner violence (IPV), the structural determinants shaping both experiences are less well understood. OBJECTIVES: The review seeks to answer: how did structural determinants of gender inequality shape both the experiences of increased unpaid care burdens and IPV during the COVID-19 pandemic lockdown? Which policy proposals might mitigate these effects during future pandemic preparedness and response? METHODS: We conducted a scoping review of two sets of literature: on COVID-19 and unpaid care and COVID-19 on IPV. Following systematic searches of key databases and the application of inclusion/exclusion criteria, we analyzed articles using a gender matrix framework to identify common themes and policy recommendations. RESULTS: Common themes include adherence to traditional gender norms, power dynamics featuring coercive control, narrowed pathways to formal and informal supports, and compounding emotional tolls. Policy recommendations from the literature aimed at addressing structural determinants of gender inequality common to both unpaid care and IPV, including expanded access to virtual support services, workplace policies that value the contributions of caregivers, enhanced engagement efforts to incorporate intersectional understandings, and funding for caregiver support services and the anti-violence sector which recognize the value of their contributions. CONCLUSIONS: Enhanced understanding of the structural determinants of gender inequality at play in experiences of unpaid care work and IPV highlights gaps in pandemic response, which overlooked the role of gender inequities in shaping relationship dynamics, as well as areas for more gender transformative policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".