“We’ve Already Endured the Trauma, Who is Going to Either End that Cycle or Continue to Feed It?”: The Influence of Family and Legal Systems on Native American Women’s Intimate Partner Violence Experiences
Bibliographic record
Abstract
Native American (NA) women experience higher rates of intimate partner violence (IPV) compared to other U.S. racial/ethnic groups, yet previous research has not sufficiently examined the complex determinants shaping their IPV experiences. This research explores the interplay of family networks and legal systems influencing NA women's IPV experiences. Data were collected through in-depth individual and group interviews with 42 NA survivors and 41 health/social service providers from July 2016 to June 2017 in NA communities from three different U.S. regions. We used Grounded Theory to develop emergent themes from the data, focusing on system-level risk and protective factors of women's of IPV experiences. In terms of family systems, participants indicated that NA communities were comprised of highly influential and interwoven family systems, making them powerful sources of support for both survivors and their partners who use violence. Participants described how intergenerational violence exposures contributed to the normalization of violence. In terms of legal systems, participants described inconsistent consequences for abusers of NA women, insufficient protection from legal systems, and manipulation of jurisdictional complexities. Interactions between family and legal systems influenced decision-making and outcomes. Family and community-based approaches, and the incorporation of traditional language and cultures, are needed to promote healing. Our findings reflect the complex ways that family and legal systems shape NA women's IPV experiences. Results provide insight into how NA women interact with and navigate these systems when experiencing IPV and how these systems impact decision-making and the ability to be safe from IPV. Research is needed to advance understanding of the inter-relationships between intergenerational trauma, family systems, and legal systems on IPV survivors' mental health and wellness. To make meaningful change, further research examining IPV from an interdisciplinary perspective that explores the interplay of social determinants of health inequities is needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".