Parenting Stress and Coping Strategies Adopted by Working Mothers: A Thematic Research

Abstract

This study aims at examining the level of parenting stress among working mothers and ways rationalized to balance between paid employment and family roles. The analysis of stress and coping is done with the help of thematic analysis with Decision Tree, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN) algorithms. For this, surveys with fixed questions were administered and the data obtained was pre-processed for measuring the efficacy of each algorithm for stress indicants categorization. From the experiment, Random Forest outperforms with a 94.3% accuracy rate while the next highest accuracy rate is at 91.7% with SVM. The results presented here show that by training algorithmic models with the identified qualitative themes and behavioral indicators, the patterns of parenting stress can be hence identified and predicted. The thematic analysis revealed five dominant stressors: conflicts concerning time management, lack of support at the workplace and for family especially elderly parents, coping with the work-family conflict, feeling guilty after returning to work, and perceived emotional exhaustion. Strategies for coping were social reinforcement, time management, self-care measures, and cognitive restructuring. AI-based solutions can also provide a new perspective on the concept of psychological stress and the means to improve support systems for working mothers in this domain. This research finding to academic knowledge and also to practice efforts that target enhancement of maternal mental health and work-family balance