ArticleBMC public health2026
Associations of employment status with social media addiction, anxiety, and parenting self-efficacy among mothers of infants: a cross-sectional study using machine learning.
Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundDifferences in social media addiction, anxiety, and parenting self-efficacy according to maternal employment status have important implications for maternal and child public health. However, evidence comparing these psychosocial outcomes between employed and non-employed mothers of infants remains limited, hindering the development of targeted maternal and child health interventions. This study compared social media addiction, anxiety, and parenting self-efficacy between employed and non-employed mothers and evaluated the performance of machine learning models for classifying employment status.
methodsThis descriptive cross-sectional study included 462 mothers with children aged 0-24 months (231 employed and 231 non-employed). Data were collected between November 2023 and February 2024 using the Social Media Addiction Scale, the Perceived Maternal Parenting Self-Efficacy Scale, and the Generalized Anxiety Disorder-7 Scale. Group differences were assessed using independent-samples t-tests, followed by analysis of covariance (ANCOVA) adjusted for age, education level, and socioeconomic status, and several supervised machine learning algorithms were used to classify employment status.
resultsNon-employed mothers had significantly higher social media addiction and parenting self-efficacy scores than employed mothers (both p < 0.001), whereas anxiety scores did not differ significantly (p = 0.655). Among the machine learning models, XGBoost showed the best classification performance, and SHAP analysis identified parenting self-efficacy as the most important feature contributing to employment status classification.
conclusionEmployment status was associated with differences in social media addiction and parenting self-efficacy, but not anxiety, among mothers of infants. Machine learning approaches may complement conventional statistical analyses by identifying variables that contribute most to employment status classification. Given the cross-sectional design, these findings should be interpreted as associations rather than evidence of causal relationships.
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