Intimate Partner Violence Against Women in the Arab Countries: A Systematic Review of Risk Factors
Bibliographic record
Abstract
Intimate partner violence (IPV) profoundly damages physical, sexual, reproductive, and psychological health, as well as social well-being of individuals and families. We sought in this systematic review to examine the risk factors according to the integrative ecological theoretical framework for IPV for women living in the Arab countries. We searched Embase, PubMed, PsycINFO, and SCOPUS, supplemented by hand searching of reference lists. A research strategy was developed and observational studies were included if they considered female participants (age ≥13) in heterosexual relationships, estimates of potential risk factors of IPV, and IPV as a primary outcome. We conducted a narrative synthesis of the risk factors data from 30 cross-sectional studies. Factors associated with increased IPV against women were extracted and categorized into four levels according to the updated integrative ecological model. At the individual level, risk factors were either related to victims or perpetrators of IPV. Factors relating to marriage, conflict within the family, etc., were explored and included within the family level, whereas factors relating to the extended family and the nature of marriage were included in the community level. Finally, risk factors relating to the cultural context that are influenced by the political and religious backgrounds were included in the societal level. The complex structure of violence against women in the Arab world calls for socioculturally sensitive interventions, which should be accompanied by systematic and structured work aimed at improving Arab women’s status at all levels.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".