Epidemiological profile of women victims of domestic violence in Casablanca
Bibliographic record
Abstract
Abstract Introduction Violence against women is a major public health problem because, whatever its form, it has an impact on the physical and psychological health of victims. Objectives To describe the socio-demographic characteristics of the victims and their partners as well as the different forms of violence observed and their impact on the victims. Participants and Methods This was a descriptive cross-sectional study conducted from January to March 2019 in two associations providing assistance to women victims of domestic violence. Results Our study collected 102 women whose average age was 33 +/- 10.8 years (18-67 years), 77% resided in urban areas, 54% had left the marital home. Regarding marriage, 86% were married and 37% had been married for more than 10 years. The average number of dependent children was 1.75 +/-1.3. Regarding employment status, 65% were housewives and 61% had no income; a quarter of the victims had a history of domestic violence. Regarding their spouses, the average age was 40 years old, 9% were unemployed; 41% had consumed alcohol at the time of the violence and one third of them had taken drugs. Physical and psychological violence were present in almost all cases, respectively 99% and 97%, economic violence in 73% of cases and sexual violence in 69% of cases. These incidents occurred mainly in the context of conflict with in-laws (66.7%). Conclusions Despite the measures undertaken by the authorities, domestic violence is a scourge that is not limited to any particular profile and has complex triggering mechanisms. Key messages Domestic violence is still a scourge today. The identification of strategic axes of prevention is essential.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".