Partners' controlling behaviors and intimate partner sexual violence among married women in Uganda
Bibliographic record
Abstract
Studies on the association between partners' controlling behaviors and intimate partner sexual violence (IPSV) in Uganda are limited. The aim of this paper was to investigate the association between IPSV and partners' controlling behaviors among married women in Uganda. We used the 2011 Uganda Demographic and Health Survey (UDHS) data, and selected a weighted sample of 1,307 women who were in a union, out of those considered for the domestic violence module. We used chi-squared tests and multivariable logistic regressions to investigate the factors associated with IPSV, including partners' controlling behaviors. More than a quarter (27%) of women who were in a union in Uganda reported IPSV. The odds of reporting IPSV were higher among women whose partners were jealous if they talked with other men (OR = 1.81; 95% CI: 1.22-2.68), if their partners accused them of unfaithfulness (OR = 1.50; 95% CI: 1.03-2.19) and if their partners did not permit them to meet with female friends (OR = 1.63; 95% CI: 1.11-2.39). The odds of IPSV were also higher among women whose partners tried to limit contact with their family (OR = 1.73; 95% CI: 1.11-2.67) and often got drunk (OR = 1.80; 95% CI: 1.15-2.81). Finally, women who were sometimes or often afraid of their partners (OR = 1.78; 95% CI: 1.21-2.60 and OR = 1.56; 95% CI: 1.04-2.40 respectively) were more likely to report IPSV. In Uganda, women's socio-economic and demographic background and empowerment had no mitigating effect on IPSV in the face of their partners' dysfunctional behaviors. Interventions addressing IPSV should place more emphasis on reducing partners' controlling behaviors and the prevention of problem drinking.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".