Domestic Violence in Mexico: Perspectives of Mexican Counselors
Bibliographic record
Abstract
Drawing on professional experiences of Mexican counselors, we examined the lives of women who experienced domestic violence from the perspective of those counselors working most closely with them. Although there are many studies that have investigated the origin and perpetuation of domestic violence in Mexico, few have included the participation of counselors. In the present study, we identify common antecedents of abuse from the perspective of counselors and note whether these antecedents match those identified in the literature from the victim’s perspective. Economic resources, psychological treatment, social support, and an awareness of their situation are important factors identified by the counselors in moving women involved in domestic violence toward a positive resolution. According to counselors, economic and emotional dependence, a distorted view of family violence, partner’s addiction to alcohol, social and family pressure, and specific cultural patterns may predict negative outcomes. Differences in antecedents identified by counselors were found in comparison to antecedents identified in literature from the victim’s perspective. The present study revealed that alcohol may decrease inhibition in people who are already prone to use violence. In addition, the participants in this study pointed out other successful resolutions, which are not documented in previous studies. These resolutions are brought about through separation from the abusive partner and help from the legal system.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".