Examining the gender equity outlook and patriarchal beliefs of police constables in Allahabad, India: A machine learning approach

Abstract

This cross-sectional study examined the association between gender equity outlook (GEO) and patriarchal beliefs (PB) among 190 police constables in Allahabad, India. Results of linear regression and the scatter plot analysis revealed an inverse association (ß= −0.33, 95% confidence interval [CI], 0.16–0.49) between the GEO and PB of police constables. The feature selection technique of machine learning was also used to understand which socio-demographic characteristics were most important in explaining the GEO and PB. Recursive features elimination, a decision tree, random forest, ridge, and lasso regression showed gender as the most important feature (GEO ß= 3.66, 95% CI, P < 0.00; PB ß= 9.54, 95% CI, P < 0.00) followed by age and education in explaining the prevalence of GEO and PB. The findings underscore the importance of (re)shaping policing policies and interventions with a particular focus on gender equity and equality in eliminating the prevalent patriarchy among police in India.