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The objective of this paper is to suggest a mathematical model of unpaid housework and empirically test its main predictions using data from Mexico (2014) and Colombia (2017) time-use surveys. The model, based upon the Marxist-feminist approach, suggests that the magnitude of unpaid housework is determined by the super-exploitation of labor, i.e. the gap between wages and the value of labor-power. The outcome is an equation that relates positively the magnitude of unpaid housework with the super-exploitation of labor. The parametric and nonparametric regression estimates applied in both countries show preliminary empirical support for the theoretical model. The theoretical and empirical findings have several implications for Marxist-feminist literature as well as for empirical research on unpaid housework. [ABSTRACT FROM AUTHOR]

Researchers and practitioners recognise the importance of context when implementing healthcare interventions, but the influence of wider environment is rarely mapped. This paper identifies the country and policy-related factors potentially explaining the country differences in outcomes of an intervention focused on improving detection and management of heavy alcohol use in primary care in Colombia, Mexico and Peru. Qualitative data obtained through interviews, logbooks and document analysis are used to explain quantitative data on number of alcohol screenings and screening providers in each of the countries. Existing alcohol screening standards in Mexico, and policy prioritisation of primary care and consideration of alcohol as a public health issue in Colombia and Mexico positively contributed to the outcome, while the COVID-19 pandemic had a negative impact. In Peru, the context was unsupportive due to a combination of: political instability amongst regional health authorities; lack of focus on strengthening primary care due to the expansion of community mental health centres; alcohol considered as an addiction rather than a public health issue; and the impact of COVID-19 on healthcare. We found that wider environment-related factors interacted with the intervention implemented and can help explain country differences in outcomes. [ABSTRACT FROM AUTHOR]