‘We have tried to remain warm despite the rules.’ Domestic violence and COVID-19: implications for shelters’ policies and practices
Bibliographic record
Abstract
This article presents findings from a study that investigated the impacts of the COVID-19 pandemic on domestic violence shelters’ policies and practices. This study was conducted in partnership with feminist organisations in two regions in the Quebec, Canada. Qualitative data were collected from nine domestic violence shelters, using a web-based questionnaire. Thematic content analysis was conducted using NVivo. The research findings reveal that the COVID-19 pandemic has created significant challenges for shelters, as they have had to ensure women’s and children’s safety while preventing the spread of the virus. In this context, they have had to adapt their services and practices, and it has sometimes been difficult to maintain their feminist approach. Nonetheless, shelters have been creative and have developed multiple strategies to overcome these challenges and to ensure women’s and children’s access to services. The research findings contribute to our understanding of the impacts of the COVID-19 pandemic, and highlight the essential role that these organisations have played to ensure women’s and children’s safety at a time when they have been particularly vulnerable.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.034 | 0.044 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".