Prevalence and Risk Factors of Domestic Violence Against Women by Their Husbands in Iran
Bibliographic record
Abstract
OBJECTIVE: Domestic violence against women is a health problem. Research on domestic violence in order to clarify the relationship between the different forms of violence and health outcomes is needed. This study aimed to determine the frequency and risk factors of domestic violence in women. It also assessed the association between risk factors and psychological, physical, and sexual violence against women by their intimate partners. MATERIALS & METHODS: This cross-sectional study was done on married women 16-80 years of age living in jahrom south of Iran between August 2013 and December 2014. This research was implemented through questionnaires including the demographic characteristic. The form of partner violence including emotional abuse, physical violence and sexual violence was assessed with a validated questionnaire. Odds ratios and 95% confidence intervals were calculated to measure the association between violence and factors. RESULTS: The prevalence of physical, sexual and emotional domestic violence was respectively 16.4%, 18.6% and 44.4%.and was associated with Age (p=0.002), Husband's Age (p=0.001), Length of marriage (p=0.002), Woman's low educational level women's education (OR=4.67 95%.CI=1.97-11.07), husband's low education (OR=9.22 95%. CI=0.69-12.16), were the most important risk factors for violence. CONCLUSION: Prevalence of physical, emotional or sexual violence was very high. Men's violence against women in intimate relationships is commonly occurring in Iran. Considering the factors contributing to violence against women, raising the level of education of men and women is one of the ways to prevent violence.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".