Prevalence and Correlates of Intimate Partner Violence Among Women and Men in Mexico: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
This article presents the first systematic review and meta-analysis of the prevalence and correlates of different forms of intimate partner violence (IPV) among women and men in Mexico. To identify studies, a comprehensive search strategy was developed and executed across 11 databases (Academic Search Complete, APA PsycInfo, CINAHL, Cochrane CENTRAL, Embase, International Bibliography of the Social Sciences, LILACS, MEDLINE, SciELO, Sociological Abstracts, Web of Science). From the 1,746 studies screened, 155 full-text articles were reviewed, and this systematic review included 27 studies involving 249,557 participants to determine the prevalence of physical, psychological, sexual, threats, and other forms of IPV, according to gender and other sociodemographic characteristics. Overall IPV prevalence was 16.4%, with significant differences across pregnant and non-pregnant women. Physical IPV prevalence was 14.7%, revealing higher rates in men (29.5%) compared to women (14.2%). Psychological IPV prevalence was 27.3% and sexual IPV was at 6.6%, with differences across evaluation periods. Threats and other IPV forms showed a prevalence rate of 14.2% and 21.5%. Meta-regression analyses included gender, education, marital status, rural residency, pregnancy, age, and evaluation period. This study demonstrates that IPV is a critical public health concern in Mexico, impacting both women and men. It shows the vulnerability of rural residents, youth, and pregnant women. However, understanding IPV complexities in Mexico requires nuanced considerations of demographic and situational contexts. Urgent initiatives from municipal, state, and federal governments are needed to combat IPV, focusing on prevention and support for affected individuals.
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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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".