Machine Learning Based Classification On Work Life Balance Among Women Teachers In Higher Education

Stress is a prevalent and significant global health concern that arises from the interaction between an individual and their surrounding environment. The attainment of work-life balance is a significant objective for women in the workforce. In the absence of job satisfaction and a consistent lifestyle, female employees may encounter a predicament. In the current societal context characterized by rapid acceleration, educational institutions are actively seeking strategies to positively impact the overall performance of their teachers. This includes efforts to boost the morale of female teachers and improve faculty retention rates. Work-life balance (WLB) is widely recognized as one of the most crucial factors that significantly impact an organization's overall effectiveness. Women experience higher levels of stress compared to men due to the additional responsibilities they typically shoulder. These responsibilities include caring for children, family members, and managing household tasks, in addition to their professional obligations. In the context of India, it has been observed that a significant proportion, specifically over 85%, of women experience heightened levels of stress as a result of managing familial responsibilities alongside their professional commitments. Severe problems can arise if appropriate measures are not implemented in response. In both institutional and domestic settings, the issue of work-life balance is increasingly becoming a top priority for employers and employees alike. The main aim of this study is to evaluate the specific type of WLB experienced by women school teachers in the Kumbakom district of Tamil Nadu. To support this objective, a classification was conducted utilizing machine learning algorithms to evaluate the Work Life Balance Indicator of professionals. The aim is to determine the status of work life balance in this specific context. Based on an analysis of general and marital status, no substantial differences have been observed in the quality of WLB among teachers.

Authors

Publication Details

Journal
American Journal of Psychiatric Rehabilitation
Published
2025-04-09
DOI
https://doi.org/10.69980/ajpr.v28i1.83
Primary Topic
Sleep and Work-Related Fatigue
Type
article
Field-Weighted Citation Impact
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article

Machine Learning Based Classification On Work Life Balance Among Women Teachers In Higher Education

B Logeswari
American Journal of Psychiatric Rehabilitation
Sleep and Work-Related Fatigue
article

Machine Learning Based Classification On Work Life Balance Among Women Teachers In Higher Education

B Logeswari
article en

Abstract

Stress is a prevalent and significant global health concern that arises from the interaction between an individual and their surrounding environment. The attainment of work-life balance is a significant objective for women in the workforce. In the absence of job satisfaction and a consistent lifestyle, female employees may encounter a predicament. In the current societal context characterized by rapid acceleration, educational institutions are actively seeking strategies to positively impact the overall performance of their teachers. This includes efforts to boost the morale of female teachers and improve faculty retention rates. Work-life balance (WLB) is widely recognized as one of the most crucial factors that significantly impact an organization's overall effectiveness. Women experience higher levels of stress compared to men due to the additional responsibilities they typically shoulder. These responsibilities include caring for children, family members, and managing household tasks, in addition to their professional obligations. In the context of India, it has been observed that a significant proportion, specifically over 85%, of women experience heightened levels of stress as a result of managing familial responsibilities alongside their professional commitments. Severe problems can arise if appropriate measures are not implemented in response. In both institutional and domestic settings, the issue of work-life balance is increasingly becoming a top priority for employers and employees alike. The main aim of this study is to evaluate the specific type of WLB experienced by women school teachers in the Kumbakom district of Tamil Nadu. To support this objective, a classification was conducted utilizing machine learning algorithms to evaluate the Work Life Balance Indicator of professionals. The aim is to determine the status of work life balance in this specific context. Based on an analysis of general and marital status, no substantial differences have been observed in the quality of WLB among teachers.

American Journal of Psychiatric Rehabilitation
Gender equality
Openalex Percentile: Top 93%
Sleep and Work-Related Fatigue
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Machine Learning Based Classification On Work Life Balance Among Women Teachers In Higher Education — B Logeswari · American Journal of Psychiatric Rehabilitation (2025) | TGRS Research Map | TGRS