Bibliographic record
Abstract
This study bridges a knowledge gap in existing literature by investigating correlates of male perpetrated intimate partner violence in Ghana. Based on a review of literature, it was hypothesized that some sociodemographic factors would be associated with the experience of male perpetrated physical, psychological, and sexual intimate violence in Ghana. To examine this hypothesis, data were collected from a convenience but nonreferral sample of 443 women, using a structured instrument. Descriptive statistics showed that 27%, 62%, and 34% of the sample, respectively, had experienced past-year physical, psychological, and sexual violence. Multivariate logistic regression analyses were performed, using some 15 sociodemographic factors as predictors. The analyses showed that women in rural settings had 1.71 and 2.20 times the odds of experiencing psychological and physical violence, respectively; women who were younger than their husbands had 2.67 and 5.71 times the odds of experiencing psychological and sexual violence, respectively; women whose husbands were unemployed had 2.41 and 2.58 times the odds of experiencing psychological and physical violence, respectively; women whose husbands had nonmarital sexual partners had 2.10 and 2.33 times the odds of experiencing psychological and physical violence, respectively; women who rated their health as good had 2.10 and 2.39 times the odds of experiencing psychological and sexual violence, respectively; and women whose husbands did not appreciate them had 2.22 and 2.57 times the odds of experiencing physical and sexual violence, respectively. Low education and polygamous marriage were also related to psychological violence. Policy and practice implications of the findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".