Help-Seeking Behaviors and Associated Factors Among Chinese Immigrant Women Experiencing Intimate Partner Violence
Bibliographic record
Abstract
Chinese immigrants are underrepresented in research on intimate partner violence (IPV). There is a lack of quantitative research on help-seeking behaviors and patterns among Chinese immigrant women experiencing IPV. In this study, using a cross-sectional design, we examine patterns of help-seeking behaviors among Chinese immigrant survivors of IPV, as well as associated factors. Participants were recruited through the WeChat and Prolific platforms to complete an online survey. A total of 139 women who reported IPV while residing in the U.S. participated. The survey’s questions addressed five forms of informal help and eight forms of formal help, as well as reasons for not seeking help. The survey also included measures of social isolation, loneliness, preferences for the ethnicity of people at social gatherings, depression, anxiety, sociodemographic characteristics, and immigration-related factors. Latent class analysis revealed two distinct help-seeking patterns. The majority either refrained from seeking help or solely relied on family and friends, while a smaller group sought support from a wider range of sources, but still primarily relied on family and friends. Factors associated with Chinese immigrant women’s use of broad sources for support included older age; being single, separated, divorced, or widowed; U.S. citizenship or permanent residency; attending social gatherings primarily with Americans; depression symptoms; and lower loneliness levels. The study’s findings underscore the importance of interventions to promote help-seeking behaviors among Chinese immigrant survivors by expanding social networks, fostering collaborations between mainstream IPV services and Chinese community organizations to implement community outreach, offering interventions that are empowerment-based, and training bilingual and bicultural service providers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".