Evaluating Community Factors Associated With Individually Held Intimate Partner Violence Beliefs Across 51 Countries.
Elyse Joan ThulinJustin E HeinzeMarc A ZimmermanPublished in: Journal of interpersonal violence (2021)
Globally, one in three women will experience intimate partner violence (IPV) within her lifetime. IPV attitudes are highly predictive of IPV. While a wealth of literature on risk factors related to IPV exist, an overarching critique in the field is the lack of studies examining risk factors across the socioecological framework. Using data from multiple administrative and individual surveys, this study fills a gap in the literature by evaluating the effect of meso-influences on physical IPV attitudes (i.e., permissibility of a man beating his wife) while accounting for known micro- and macro-risk factors in 64,466 individuals across 51 low-, middle- and high-income countries. Mixed-effects modeling was used to evaluate factors and identify comparative contributions of each factor representing the socio-ecological levels. We tested five multivariate logistic models. The final model indicated that greater perceived neighborhood disorder and less perceived neighborhood security were associated with physical IPV attitudes, while individual endorsement of interpersonal violence, belief in corporal punishment of children, holding greater patriarchal beliefs, being male, being separated from a significant partner, reporting greater household hunger and nationally lower levels of female literacy were associated with beliefs that IPV is acceptable. Overall, the findings of this study support that IPV is a complex behavior, influenced by factors across socio-ecological domains. However, data on neighborhood structural factors (i.e., exosystem) would help unpack the mechanisms between macro-, meso- and micro-level factors and may be important for protecting women from violence.
Keyphrases
- intimate partner violence
- mental health
- physical activity
- risk factors
- polycystic ovary syndrome
- electronic health record
- climate change
- big data
- type diabetes
- emergency department
- risk assessment
- data analysis
- public health
- machine learning
- hepatitis c virus
- artificial intelligence
- cross sectional
- pregnant women
- adipose tissue
- deep learning
- breast cancer risk
- adverse drug