Intimate partner violence in older South African women: An analysis of the 2016 Demographic and Health Survey
Bibliographic record
Abstract
BACKGROUND: South Africa (SA) has one of the highest rates of intimate partner violence (IPV) in the world. It is also in the midst of a demographic transition in which the number of people aged >60 years is expected to double by mid-century. Despite the confluence of these two public health issues, there are no published studies on the epidemiology and risk factors for IPV in older SA women. OBJECTIVES: To provide a foundational understanding of IPV among women aged ≥50 years in SA. METHODS: This study used the first-ever nationally representative sample of women aged >49 (N=2 265) that includes data on physical, sexual, and emotional IPV. Both lifetime experience of IPV and IPV within the past 12 months were reported, as was the presence of controlling behaviours by the partner. Four multilevel logistic models and one multilevel linear regression model were fit to examine the demographic, developmental and structural correlates of IPV in women aged 50 - 95. RESULTS: The lifetime prevalence rates for all types of IPV were slightly higher among older women than among women aged 15 - 49. Nine percent of respondents reported IPV in the past 12 months, and 35% reported at least one persistent controlling behaviour. Divorced/separated women and those who had witnessed IPV as a child had greater odds of reporting IPV. In contrast to the literature on younger women, education, race and wealth were not strong predictors of IPV in this sample of older women. CONCLUSIONS: This study is the first of its kind in the SA context, and shows that IPV is a persistent threat for women across the lifespan. It suggests that IPV may manifest differently in older women compared with women of reproductive age, necessitating future qualitative and quantitative studies that examine the correlates, causes and points of intervention unique to this growing population.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".