The COVID-19 Pandemic and Intimate Partner Violence (IPV) Among Immigrant Women in Canada: A Narrative Review
Bibliographic record
Abstract
Introduction: The COVID-19 pandemic brought myriad challenges and disruptions to societies worldwide. The impact of the pandemic on immigrant women living in Canada and who experience intimate partner violence (IPV) was very concerning during lockdowns specifically. As Canada implemented social distancing measures and stay-at-home orders to curb the spread of the virus, immigrant women—already at a heightened risk of IPV due to factors such as language barriers and immigration status—faced an increased likelihood of being trapped in abusive situations, with limited access to essential support services such as shelters, counseling centers, and legal aid. This narrative review aims to illuminate the experiences of IPV including the challenges and strategies used to cope during the pandemic among immigrant women. While this narrative review aims to obtain an extensive understanding of the IPV experiences among immigrant women during the pandemic, it also highlights the need for developing culturally sensitive interventions to address IPV challenges for immigrant women in similar contexts. A search was undertaken of multiple databases: Medline, CINHAL, Google Scholar, and PsycINFO. To identify studies related to IPV and COVID-19, various search terms were used including “abused women”, “COVID-19”, and “IPV” and their synonyms. This review employs the social–ecological model and intersectionality to illuminate the IPV experiences of immigrant women during the COVID-19 pandemic. Four themes emerged from this study including the realities of COVID-19 restrictions; IPV vulnerabilities during the COVID-19 pandemic; the impacts of IPV and the COVID-19 pandemic; and the strategies for addressing IPV (women and system perspectives). We draw on past research and the knowledge of our multidisciplinary team of researchers to recommend culturally sensitive resources and health services for abused women living in Canada as well as highlight effective intervention strategies to address the varied and complex needs of these women.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".