Q1 2026

Explicating the interplay of maternal work stress, breastfeeding, mental health, and infant attachment using machine learning and statistical modeling

Darin Mansor Mathkor
10.25259/jksus_1537_2025 390 المشاهدات 0 الاقتباسات
0
الاقتباسات
390
المشاهدات
الملخص


Maternal work stress is a major problem for working mothers, but its impact on both the continuation of breastfeeding and on maternal mental health is complex and not yet fully understood. This study used large open-access datasets from Open Science Framework (n=2,010) to examine the interplay of these complicated relationships. Statistical and machine learning methods of analysis were employed to explore the relationship between maternal work stress, sociodemographic variables, breastfeeding duration, and maternal mental health markers. Traditional regression models showed that variables like maternal education were predictive of birth weight; they accounted for relatively little variance (Polynomial R
2
= 0.24), implying that direct, linear associations fall short of representing the complete picture of maternal-child health outcomes. In contrast, a Random Forest classification model correctly predicted cessation of breastfeeding at a high rate (85%), accurately identifying intricate, non-linear patterns that are obscured by means of traditional approaches. Clustering analysis further revealed that work stress and breastfeeding are not directly related, but are moderated by different subgroups that are determined by sociodemographic factors such as access to healthcare, social support, and work culture. A high level of maternal distress was detected across all groups, pointing to a widespread public health concern. These findings emphasize the limitations of oversimplified cause-and-effect models in understanding maternal health. They underscore the need for multi-level interventions, such as workplace policies that support mothers, early mental health screening, and targeted public health campaigns. Addressing these issues is not only instrumental in enhancing breastfeeding outcomes and maternal well-being, but also integrating early work-stress screening and lactation support as a part of standard maternal care will help in identifying at-risk mothers and thereby provide personalized psychological and breastfeeding therapies for better outcomes‍​‍‌​‍​‌‍​‍‌.

الاستشهاد بهذا المقال (APA)
Darin, M. M. (2026). Explicating the interplay of maternal work stress, breastfeeding, mental health, and infant attachment using machine learning and statistical modeling. Journal of King Saud University – Science. https://doi.org/10.25259/jksus_1537_2025
أبحاث ذات صلة
Short-term wind power prediction based on IBOA-AdaBoost-RVM
Yongliang Yuan; Qingkang Yang; Jianji Ren; Kunpeng Li; Zhenxi Wang; Yanan Li; Wu · 2024
33
استشهاد
402
A parametrized approach to generalized fractional integral inequalities: Hermite–Hadamard and Maclau…
Abdelghani Lakhdari; Bandar Bin-Mohsin; Fahd Jarad; Hongyan Xu; Badreddine Mefta · 2024
20
استشهاد
397
Modulatory effects of glutamic acid on growth, photosynthetic pigments, and stress responses in oliv…
Muhammad Hamzah Saleem; Sadia Zafar; Sadia Javed; Muhammad Anas; Temoor Ahmed; S · 2024
19
استشهاد
401
Cloud spot instance price forecasting multi-headed models tuned using modified PSO
Mohamed Salb; Luka Jovanovic; Ali Elsadai; Nebojsa Bacanin; Vladimir Simic; Drag · 2024
17
استشهاد
401
16
استشهاد
403
الوصول
عرض النص الكامل عبر DOI
نُشر في
الرقم الدولي ISSN 1018-3647
الربعية Q1
درجة المؤشر القياس العربي 100
التخصص Natural Sciences
الناشر King Saud University
الدولة 🇸🇦 Saudi Arabia
عرض ملف المجلة →
المؤلفون
تفاصيل النشر
السنة 2026
اللغة English
أُضيف في 14 Jul 2026