Intimate Partner Violence in Tunisia: Emotional Abuse and Dependency, Alexithymia, and Self-Esteem in Female Victims
Bibliographic record
Abstract
Despite legislative advances in Tunisia, intimate partner violence (IPV) remains a widespread issue where cultural norms and systems often reinforce silence and male authority over women. This study aims to explore the characteristics of IPV and assess the emotional abuse, emotional dependency, alexithymia, and self-esteem among female victims. We conducted a cross-sectional observational study, involving 120 female victims of IPV who sought expertise at the Psychiatric Emergency Department in Sfax, Tunisia. The data were collected through individual medical visits using the Emotional Abuse Scale (EAS), the Emotional Dependence Questionnaire (EDQ), the Toronto Alexithymia Scale (TAS-20), and the Rosenberg Self-Esteem Scale (RSES). Of the 120 women who agreed to participate in this study, 75.8% were affected by severe emotional violence, 38.3% were emotionally dependent on their spouses, 59.2% were alexithymic, and 83.3% had low self-esteem. The emotional abuse scores were positively correlated with emotional dependency scores (p = 0.026; rho = 0.204). The presence of alexithymia among the victims was significantly correlated with their emotional dependency on their husbands (p = 0.004). Our findings confirm that considering women’s emotional profiles can have important implications for designing both preventive and effective interventions.
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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 | 0.001 | 0.000 |
| 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) | 0.002 | 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".