“When you leave your country, this is what you’re in for”: experiences of structural, legal, and gender-based violence among asylum-seeking women at the Mexico-U.S. border
Bibliographic record
Abstract
BACKGROUND: Recent U.S. immigration policy has increasingly focused on asylum deterrence and has been used extensively to rapidly deport and deter asylum-seekers, leaving thousands of would-be asylum-seekers waiting indefinitely in Mexican border cities, a large and growing proportion of whom are pregnant and parenting women. In the border city of Tijuana, Mexico, these women are spending unprecedented durations waiting under unsafe humanitarian conditions to seek safety in the U.S, with rising concerns regarding increases in gender-based violence (GBV) among this population during the COVID-19 pandemic. Given existing gaps in evidence, we aimed to describe the lived experiences of GBV in the context of asylum deterrence policies among pregnant and parenting asylum-seeking women at the Mexico-U.S. border. METHODS: Within the community-based Maternal and Infant Health for Refugee & Asylum-Seeking Women (MIHRA) study, we conducted semi-structured qualitative interviews with 30 asylum-seeking women in Tijuana, Mexico between June and December 2022. Eligible women had been pregnant or postpartum since March 2020, were 18-49 years old, and migrated for the purposes of seeking asylum in the U.S. Drawing on conceptualizations of structural and legal violence, we conducted a thematic analysis of participants' experiences of GBV in the context of asylum deterrence policies and COVID-19. RESULTS: Pregnant and parenting asylum-seeking women routinely faced multiple forms of GBV perpetuated by asylum deterrence policies at all stages of migration (pre-migration, in transit, and in Tijuana). Indefinite wait times to cross the border and inadequate/unsafe shelter exacerbated further vulnerability to GBV. Repeated exposure to GBV contributed to poor mental health among women who reported feelings of fear, isolation, despair, shame, and anxiety. The lack of supports and legal recourse related to GBV in Tijuana highlighted the impact of asylum deterrence policies on this ongoing humanitarian crisis. CONCLUSION: Asylum deterrence policies undermine the health and safety of pregnant and parenting asylum-seeking women at the Mexico-U.S. border. There is an urgent need to end U.S. asylum deterrence policies and to provide respectful, appropriate, and adequately resourced humanitarian supports to pregnant and parenting asylum-seeking women in border cities, to reduce women's risk of GBV and trauma.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".