Associating Pregnancy With Partner Violence Against Chinese Women
Bibliographic record
Abstract
The present study discusses if pregnancy is a risk factor for intimate partner violence using a large, representative sample containing detailed information on partner violence including physical and sexual abuse as well as perpetrator-related risk factors. Data from a representative sample of 2,225 men were analyzed. The self-reported prevalence of men's violence against their female partners was computed and compared in terms of demographic, behavioral, and relationship characteristics. The preceding-year prevalence of physical assault, sexual violence, and "any violence or injury" among the group whose partners were pregnant was 11.9%, 9.1%, and 18.8%, respectively. This is significantly higher than the nonpregnant group. Pregnancy was significantly associated with increased odds of violence, including physical assault, sexual violence, and "any violence or injury" (ORs = 2.42, 2.42, and 2.60, respectively). Having controlled for relationship characteristics including social desirability, social support, in-law conflict, dominance, and jealousy of male perpetrators, pregnancy was significantly associated with "any violence or injury." Demographic and behavioral variables accounted for pregnant women's significantly higher odds of having been abused in the year preceding the data collection. This study provides preliminary findings on the association between pregnancy and partner violence. Our findings underscore the need to screen for violence among pregnant women in clinical health care settings as well as in communities. Perpetrator-related risk factors should be included in the assessment of risk for partner violence against pregnant women. For the prevention of intimate partner violence, family-based intervention is needed to work with victims as well as perpetrators.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| 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".