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Background <p>Differences 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.</p> Methods <p>This 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.</p> Results <p>Non-employed mothers had significantly higher social media addiction and parenting self-efficacy scores than employed mothers (both <i>p</i> &lt; 0.001), whereas anxiety scores did not differ significantly (<i>p</i> = 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.</p> Conclusion <p>Employment 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.</p>

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Associations of employment status with social media addiction, anxiety, and parenting self-efficacy among mothers of infants: a cross-sectional study using machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月18日

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  • Mehmet Uçar,
  • Mehmet Kaplan,
  • Metin Yildiz

摘要

Background

Differences 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.

Methods

This 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.

Results

Non-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.

Conclusion

Employment 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.