Parenting practices and interventions during the COVID-19 pandemic lockdown: an exploratory cross-sectional study of caregivers in Brazil, Mexico, and the United States.

Introduction: The COVID-19 pandemic led countries' governments to rapidly establish lockdowns and social distancing, which altered family routines and the quality of family relationships worldwide.

Objectives: This exploratory cross-sectional study aimed to identify the impacts of the social distancing and lockdown in parenting practices of caregivers from Brazil, Mexico, and the USA, and to analyze the continuity of parenting intervention support for children and their families at the beginning of the pandemic in these countries.

Methods: The sample consisted of 704 caregivers of children (286 from Brazil, 225 from Mexico, and 193 from the USA) who answered an online survey about parenting practices before/after quarantine, caregiver/child routines, feelings related to quarantine, changes in everyday life since the beginning of the COVID-19 pandemic, contact with health professionals, and sources of parenting information.

Parenting Programs for Underserved Populations in Low- and Middle-Income Countries: Issues of Scientific Integrity and Social Justice.

Research suggests that parenting programs are effective for preventing behavioral and emotional difficulties in children, but a lot more attention needs to be paid to issues of context and culture during the development, testing and implementation of these interventions. The views and needs of underserved and disenfranchised communities in the US and the Global South are often not taken into account for the development and testing of interventions. The successful implementation of evidence-based interventions for vulnerable children and families in underserved and marginalized communities requires careful consideration of how existing paradigms of prevention, evaluation, and implementation science impact issues of social justice and equity.

Innovative Identification of Substance Use Predictors: Machine Learning in a National Sample of Mexican Children.

Machine learning provides a method of identifying factors that discriminate between substance users and non-users potentially improving our ability to match need with available prevention services within context with limited resources. Our aim was to utilize machine learning to identify high impact factors that best discriminate between substance users and non-users among a national sample (N = 52,171) of Mexican children (i.e.

The influence of perceived parenting on substance initiation among Mexican children.

Parents shape their children's behaviors and impact their developmental trajectories. Despite this, few studies have examined the potential relationship between child reported parenting factors and lifetime substance use and use intentions. The current study examined the potential impact of parenting factors (i.

Strengthening a Culture of Prevention in Low- and Middle-Income Countries: Balancing Scientific Expectations and Contextual Realities.

Relevant initiatives are being implemented in low- and middle-income countries (LMICs) aimed at strengthening a culture of prevention. However, cumulative contextual factors constitute significant barriers for implementing rigorous prevention science in these contexts, as defined by guidelines from high-income countries (HICs). Specifically, disseminating a culture of prevention in LMICs can be impacted by political instability, limited health coverage, insecurity, limited rule of law, and scarcity of specialized professionals.