“A Lot of Gaps…Don’t Have the Budget”: Service Providers’ Insights on Supporting South Asian Immigrant Survivors of Intimate Partner Violence
Bibliographic record
Abstract
This study explored challenges faced by service providers supporting South Asian immigrant survivors of intimate partner violence (IPV) in the United States and strategies to enhance culturally responsive, trauma-informed, survivor-centered, and accessible services. Between June 2021 and July 2023, 10 providers from mainstream and South Asian-focused agencies in Florida and Texas were interviewed. Using the Ecological Systems Theory, we examined multi-level influences on service provision. Data analysis was facilitated by ATLAS.ti software. Providers identified significant barriers hindering survivors from help-seeking, including fear, precarious immigration status, cultural norms, and economic dependency. Informal networks are initial contacts, providing mixed support. Formal organizations face ecosystem structural gaps, such as limited access to legal aid and housing, and cultural barriers, including shelter conditions and rigid procedures. Building trust with survivors is also considered crucial. Recommendations included personal outreach, cultural training, expanded culturally specific services, and cross-sector partnerships. Findings emphasize the need for culturally responsive, trauma-informed services that acknowledge survivors’ intersectional identities. Strategic collaboration between mainstream and South Asian-focused organizations is vital and policy reforms for addressing structural vulnerabilities are critical for improving help-seeking pathways. Future research should explore differences within South Asian communities to help providers develop trust-based pathways that connect informal and formal support systems.
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 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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".