Psychosocial risk and protective factors associated with perpetration of gender-based violence in a community sample of men in rural KwaZulu-Natal, South Africa
Bibliographic record
Abstract
BACKGROUND: Rates of gender-based violence (GBV) in South Africa (SA) are among the highest in the world. In societies where social ideals of masculinity encourage male dominance and control over women, gender power imbalances contribute to male perpetration and women's vulnerability. The drivers that cause men to perpetrate GBV and those that lead to HIV overlap and interact in multiple and complex ways. Multiple risk and protective factors for GBV perpetration by males operate interdependently at a number of levels; at the individual level, these include chronic anxiety and depression, which have been shown to lead to risky sexual behaviours. OBJECTIVES: (i) To examine psychosocial risk factors (symptoms of anxiety and depression) as well as protective factors (social support and self-esteem) as self-reported by a cohort of males in rural KwaZulu-Natal (KZN) Province, SA; and (ii) to determine whether there are differences in anxiety, depression, social support and self-esteem between perpetrators and non-perpetrators. METHODS: A cross-sectional study using quasi-probability cluster sampling of 13 of 28 wards in Harry Gwala District, KZN. Participants were then randomly chosen from each ward proportionate to size. RESULTS: The participants were relatively young (median age 22 years); over half were schoolgoers, and 91.3% had never married. Over 43% of the sample reported clinical levels of anxiety and depressive symptoms on the Brief Symptom Inventory. Rates of GBV perpetration were 60.9%, 23.6% and 10.0% for psychological abuse, non-sexual physical violence and sexual violence, respectively. GBV perpetration was associated with higher depression, higher anxiety, lower self-esteem and lower social support. CONCLUSIONS: Interventions to address GBV need to take modifiable individual-level factors into account.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".