Identifying The Barriers of Leaving Abusive Relationships For South Asian Immigrant Women in Canada : A Comprehensive Literature Review
Bibliographic record
Abstract
This study critically examines the complex and intersecting barriers that South Asian immigrant women in Canada face when attempting to leave abusive intimate partner relationships. Through a comprehensive literature review of seminal qualitative studies, this research identifies key cultural, familial, and systemic obstacles—including family honour, economic dependency, social isolation, and systemic distrust—that significantly impede survivors’ help-seeking behaviors and access to appropriate support services. The analysis exposes critical gaps in culturally competent programming, systemic biases within legal and institutional frameworks, and immigration-related vulnerabilities that further marginalize these women. Central to the study is an intersectional understanding of how race, culture, gender, and immigrant status shape experiences of intimate partner violence (IPV) within South Asian communities. Guided by anti-oppressive and feminist theoretical frameworks, this review advocates for culturally informed, trauma-informed, and intersectional approaches in social work policy, practice, and service delivery. Recommendations include developing individualized, culturally sensitive care plans, peer-led support groups, accessibility-driven service models incorporating interpreters and cultural brokers, and mandatory professional training on anti-oppressive practices. Policy reforms aimed at mitigating immigration-related barriers and expanding linguistically and culturally appropriate services are emphasized. The study further highlights the urgent need for enhanced research employing longitudinal and community-based participatory action research (PAR) methodologies to empower survivors as active participants and co-researchers, fostering agency and resilience. Ultimately, sustained advocacy, community-driven interventions, and systemic reform are essential to dismantle the structural barriers sustaining IPV within South Asian immigrant communities in Canada and to promote equity, safety, and social justice for survivors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.023 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".