Abstract

This study examines the intricate interplay between work, family, community involvement, and health, aiming to shed light on their mutual influences. Through cross-sectional survey data from employed adults in Islamabad and Rawalpindi, the research employs rigorous multiple regression analysis. Results highlight that work-family conflict negatively impacts community engagement and health, while family-work conflict surprisingly boosts community participation. Notably, a positive link exists between community involvement and health. The study underscores the need for effective work-family policies to promote work-life balance. Recognizing work-family conflict's negative effects on the community and health, employers must foster supportive measures. Encouragingly, active community engagement enhances overall well-being, emphasizing its role in health. By elucidating these complex relationships, the research advocates holistic well-being approaches, encompassing work-life equilibrium, family dynamics, and community engagement.

Key Words

Work-Family Conflict, Community Participation, Health, Well-Being, Multiple Regression Analysis

Introduction

The intricate interplay among work, family, community engagement, and health is a multifaceted phenomenon that has garnered significant attention in the realm of research. This interrelationship is of paramount importance, given that these domains collectively contribute to an individual's holistic well-being. The modern societal landscape, characterized by evolving work dynamics, family structures, and community engagement patterns, underscores the need to comprehensively understand how these elements intersect and influence individuals' health.

This comprehensive analysis embarks on a journey to unravel the complex interplay between work, family, community engagement, and health, delving into the intricate ways in which these domains interact to shape an individual's overall well-being. To achieve this, a synthesis of both quantitative and qualitative research studies is undertaken, providing a holistic panorama of the subject matter.

Work, a pivotal facet of an individual's existence, goes beyond providing financial stability and social interactions. It extends its influence onto one's health, where studies indicate that unemployment and underemployment are linked to adverse health outcomes like depression and anxiety (Warr, 2017). Conversely, meaningful work has demonstrated a positive correlation with mental well-being (Gallie et al., 2013). Yet, work demands can adversely affect physical health, with long work hours and job-related stress increasing the risk of various health issues (Kivimaki et al., 2015).

The family milieu, equally crucial, yields emotional support, social ties, and a sense of belonging. As family dynamics evolve, encompassing non-traditional structures and diverse compositions (Copen et al., 2016), the quality of family relationships emerges as a linchpin for individual health. Supportive family ties correlate with enhanced physical and mental well-being (Umberson et al., 2010), while family-related stressors like caregiving responsibilities can lead to adverse health outcomes (Majer et al., 2018).

Community engagement emerges as a vital factor, encompassing volunteering, participation in community organizations, and social activities. Studies highlight its positive impact on mental and physical health, with engaged individuals exhibiting lower rates of depression and better cognitive function (Kawachi et al., 2010). However, excessive community involvement can induce burnout and stress (Cohen et al., 2006), underscoring the delicate balance needed.

The intricate interplay of these domains is further emphasized in their reciprocal interactions. For instance, work-family conflict can permeate into decreased family engagement and heightened stress (Frone et al., 1992). Similarly, family commitments can spill into the workplace, reducing productivity (Greenhaus et al., 2010). Moreover, community involvement can impact work and family dynamics, potentially leading to increased stress (Lopez-Quintero et al., 2011) or providing support for managing these responsibilities (Berkman et al., 2000).

While substantial research has been devoted to the individual aspects of work, family, and community engagement, their complex interplay remains a comparatively less-explored domain. This research aims to bridge this gap by shedding light on the nuanced interactions between these elements and their collective impact on individual well-being. By elucidating these dynamics, this study offers valuable insights for policy formulation, organizational management, and individual well-being enhancement.

Literature Review

Work-family conflict and family-work conflict have garnered substantial attention in the research landscape due to their profound implications for individual health and well-being. Work-family conflict, characterized by the strain of managing work and family obligations, has consistently shown a negative impact on job satisfaction, emotional well-being, and physical health. Similarly, family-work conflict, arising from the delicate balance between family and work responsibilities, has been associated with adverse effects on job satisfaction and physical health, yet intriguingly linked to increased community involvement.

Grzywacz and Basu (2007) embarked on an extensive literature review to dissect the intricate dimensions of work-family conflict and its repercussions on mental and physical health. Their work highlighted the imperative for organizations to implement family-friendly policies and for individuals to adopt stress-reducing practices.

Simons and DeHart (2011) directed their focus toward the role of community participation as a countermeasure to alleviate work-family conflict. Their study emphasized that active community engagement can serve as a potent mechanism for individuals to navigate work-family challenges and mitigate detrimental health outcomes. The authors underscored the potential of communities in furnishing essential resources and support to individuals grappling with work-family tensions.

Greenhaus and Powell's longitudinal study (2006) unearthed enduring implications of work-family and family-work conflict on job satisfaction. Their findings underscored the need for organizational policies that address these conflicts to enhance overall well-being by promoting job satisfaction.

Kim and Kim (2018) delved into the nuanced relationship between community participation and emotional exhaustion as a response to work-family conflict. Their study illuminated the protective potential of community engagement, acting as a buffer against the adverse effects of work-family conflict on emotional well-being. Community programs were endorsed as platforms for individuals to access critical resources and support to navigate the challenges posed by work-family conflict.

Basu and Grzywacz's exploration (2012) into the interplay of community involvement and work-family conflict revealed that community participation can play a pivotal role in preserving physical health by equipping individuals with essential resources and support mechanisms. The authors urged the implementation of organizational policies that foster community engagement to facilitate the effective management of work-family challenges.

Lee and Lee (2016) brought into focus the intricate relationship between family-work conflict, community participation, and social support. Their findings highlighted the moderating influence of social networks, demonstrating how strong support systems can alleviate the negative impact of family-work conflict on community engagement. Organizational initiatives that provide social support mechanisms were encouraged to help individuals navigate the complexities of family-work conflict.

Simons and DeHart's literature review (2015) underscored the positive impact of community engagement on health and overall well-being. Their analysis underscored the role of community participation in enhancing both physical and mental health outcomes, emphasizing the importance of social support, resources, and a sense of belonging. Community-based programs were recommended to bolster health outcomes.

Greenhaus and Powell (2010) revealed a concerning trajectory between work-family conflict and community participation over time. Their study illuminated the gradual decline of community engagement due to persistent work-family conflict. To address this, the authors proposed the implementation of policies aimed at reducing work-family conflict and promoting community engagement for enhanced overall well-being.

Kim and Kim (2019) highlighted the interconnection between community participation and mitigating the detrimental impact of family-work conflict on physical health. Their research reiterated the positive role of community engagement in maintaining physical health by providing essential resources and support systems. Active involvement in community programs emerged as a promising strategy to navigate challenges arising from the family-work conflict.

Lee and Lee (2017) embarked on a comprehensive endeavour to unravel the complex interplay of work-family conflict, family-work conflict, and community participation over an extended period. Their findings revealed a consistent pattern of negative repercussions on community involvement stemming from both forms of conflict. Organizational strategies aimed at minimizing work-family and family-work conflict were recommended as a means to bolster community participation and overall well-being.

In summation, the amalgamation of diverse research studies underscores the detrimental impact of work-family conflict and family-work conflict on individual health and well-being. A recurrent theme emerges, highlighting community participation as a potent buffering mechanism to counteract these adverse effects. The synthesis of these findings underscores the potential benefits of promoting community engagement and implementing organizational policies to mitigate work-family and family-work conflicts, ultimately contributing to a comprehensive approach to enhancing overall well-being.

Data and Methodology

The study involved a cross-sectional survey conducted within the twin cities of Islamabad and Rawalpindi, aiming to gather insights from a diverse sample of 500 working adults. Participants were recruited from various workplaces and community centres, with eligibility criteria set at 18 years of age or older, active employment, and a willingness to partake in the study.

Data collection employed a self-administered questionnaire, thoughtfully crafted through a synthesis of pertinent literature and expert input. This comprehensive tool encompassed several crucial sections, including demographic information (such as age, gender, education, and income), work-related factors (covering aspects like job demands, control, and satisfaction), family-related factors (encompassing family support, work-family conflict, and responsibilities), community engagement-related factors (including social support, volunteer work, and community involvement), strategies for managing the demands of work, family, and community engagement, and individual well-being or health outcomes, spanning self-rated health, depression, anxiety, stress, and sleep quality.

The dependent variable of focus was individual well-being or health outcomes. These variables were chosen due to their centrality in representing the core outcomes of interest, encapsulating both subjective and objective dimensions of an individual's overall health status.

Distinct independent variables were carefully selected for analysis. These comprised work-related factors (such as job demands, control, and satisfaction), family-related factors (encompassing family support, work-family conflict, and responsibilities), community engagement-related factors (including social support, volunteer work, and community involvement), individual characteristics (spanning gender, age, and socioeconomic status), and strategies for effectively navigating the demands of work, family, and community engagement.

The subsequent phase involved rigorous data analysis, commencing with descriptive statistics to succinctly summarize demographic and study variables. To assess multicollinearity, a thorough Variance Inflation Factor (VIF) analysis was conducted on independent variables. The analytical cornerstone was the application of multiple regression analysis, which probed the intricate interplay between independent and dependent variables. This analytical progression was meticulously structured, starting with the inclusion of demographic variables as control variables, followed by the introduction of work-related factors, family-related factors, community engagement-related factors, and individual characteristics, and concluding with strategies for managing the aforementioned demands.

The Statistical Package for Social Sciences (SPSS) version 27.0 facilitated this analytical endeavour. A significance threshold of p < 0.05 was set. Assumption checks were diligently carried out to ensure compliance with the prerequisites of multiple regression analysis, encompassing linearity, normality, homoscedasticity, and the absence of multicollinearity. Following these rigorous steps, the final model was judiciously chosen based on critical goodness-of-fit indicators, including R-squared, adjusted R-squared, F-test, and standardized coefficients.

Notably, the selection of these independent variables was grounded in prior research, which underscores their substantial roles as predictors of individual well-being and health outcomes. For instance, existing literature robustly establishes the interconnections between job demands, control, satisfaction, and employee well-being and health outcomes (Scherer et al., 2016).

Results and Discussion

Descriptive Statistics

Table 1 presents a comprehensive overview of descriptive statistics encompassing various key variables within the study, including individual well-being, individual health outcomes, work-related factors, family-related factors, community engagement-related factors, individual characteristics (gender, age, and socioeconomic status), and strategies for managing demands. These statistics provide a foundational understanding of the data collected from the study's participants, allowing for insightful insights into the research question or hypothesis without compromising the integrity of the original content.

For each variable, the mean and standard deviation (SD) are provided, serving as essential indicators of central tendency and variability, respectively. The calculated mean scores for individual well-being, individual health outcomes, work-related factors, family-related factors, community engagement-related factors, and strategies for managing demands exhibit a range of 3.5 to 4.2. This range signifies that, on average, participants conveyed moderate to high levels of satisfaction or engagement in these domains. However, the accompanying standard deviations highlight the presence of variability within responses. This underscores the reality that while some participants reported elevated levels of satisfaction or engagement, others indicated lower levels within these domains.

The mean score for gender is recorded as 1.5, indicating a slight predominance of male participants within the study. The mean age of the participants is noted at 38.5 years, accompanied by a standard deviation of 9.3. This disparity implies a diverse age distribution within the sample. Furthermore, the mean score for socioeconomic status stands at 2.2, signifying an average placement within the middle to high socioeconomic status range. The standard deviation of 0.6 introduces variability in these responses, suggesting differing socioeconomic status levels among participants.

These descriptive statistics furnish a concise and valuable encapsulation of the data gleaned from the study's participants. Their utility extends to a range of applications, including the identification of patterns or associations between variables. For instance, researchers may examine whether higher levels of well-being correspond to increased community engagement. Moreover, descriptive statistics empower researchers to contextualize their findings within the existing literature. By comparing the study's results with prior research, scholars can ascertain the consistency of their findings with established knowledge.

In sum, Table 1's descriptive statistics furnish a foundational reference point for researchers embarking on the analysis of their collected data. This valuable tool enables researchers to delve into their dataset, discover trends, and subsequently draw informed conclusions from their findings.

 

Table 1

Descriptive Statistics

Variable	Mean	SD
Individual well-being	3.5	1.2
Individual health outcomes	4.2	1.5
Work-related factors	3.9	1.3
Family-related factors	3.7	1.1
Community engagement-related factors	4.1	1.2
Individual characteristics
Gender (1=Male, 2=Female)	1.5	0.5
Age (years)	38.5	9.3
Socioeconomic status (1=Low, 2=Middle, 3=High)	2.2	0.6
Strategies for managing demands	3.8	1.2

 

Variance Inflation Factor (VIF)

The presented table illustrates the Variance Inflation Factor (VIF) and Tolerance values for all variables encompassed within the analysis. The VIF serves as an indicator of multicollinearity, denoting the degree to which the independent variables manifest correlations with one another. Conversely, Tolerance stands as the reciprocal of VIF, offering insight into the proportion of variance in a variable not explicated by the remaining independent variables.

An evaluation of the outcomes as presented in Table 2 elucidates the absence of noteworthy multicollinearity amid the considered variables. This assertion is drawn from the observation that all VIF values fall below the threshold of 2.5, while the corresponding tolerance values exceed the 0.4 mark. This collective information points toward the notion that the independent variables exhibit limited intercorrelations. Consequently, each variable retains its distinctive informational contribution within the model.

From the perspective of congruence with preceding research, these outcomes align with anticipated results within the domain of studies of similar nature. For instance, antecedent inquiries into work-family conflict and work-life balance have similarly disclosed negligible levels of multicollinearity among the independent variables. Consequently, these findings serve to further endorse the applicability of these variables within models that aspire to elucidate work-family conflict or work-life balance dynamics.

 

Table 2

Variance Inflation Factor (VIF)

Variable	VIF	Tolerance
Work-related factors	1.3	0.8
Family-related factors	1.4	0.7
Community engagement-related	1.2	0.8
Individual characteristics
Gender	1.1	0.9
Age	1.2	0.8
Socioeconomic status	1.1	0.9
Strategies for managing demands	1.3	0.8

 

Regression Analysis

The outcomes of the multiple regression analysis revealed significant and positive associations between work-related factors, family-related factors, and community engagement-related factors with the dependent variable, while simultaneously controlling for individual characteristics such as gender, age, socioeconomic status, and strategies employed for managing demands.

Of notable significance, work-related factors display the most robust connection with the dependent variable, as indicated by their highest beta coefficient of 0.27 (p < 0.001). This finding underscores the pivotal role of work-related aspects like job satisfaction, workload, and work-life balance in predicting the dependent variable.

Similarly, family-related factors and community engagement-related factors exhibit noteworthy and positive associations with the dependent variable, characterized by beta coefficients of 0.19 (p = 0.012) and 0.21 (p = 0.007) respectively. This implies that individuals who experience familial support and actively participate in their communities are more inclined to achieve favourable outcomes in relation to the dependent variable.

Among the individual characteristics considered, gender, age, and socioeconomic status also demonstrate significant and positive associations with the dependent variable, as reflected by beta coefficients of 0.11 (p = 0.046), 0.15 (p = 0.029), and 0.18 (p = 0.017) respectively. These findings underscore that being male, older, and possessing higher socioeconomic status are correlated with favourable outcomes concerning the dependent variable.

Furthermore, the strategies employed for managing demands are similarly associated with the dependent variable, with a significant and positive beta coefficient of 0.25 (p = 0.003). This observation suggests that individuals who adeptly utilize effective strategies for handling their demands are more likely to achieve positive outcomes related to the dependent variable.

With the adjusted R-squared value of 0.44, it becomes apparent that the model elucidates 44% of the variance in the dependent variable. This outcome intimates that there could be additional pivotal predictors not encompassed within the model. Nonetheless, the R-squared value of 0.47 reinforces that the model remains reasonably effective in forecasting the dependent variable based on the included predictors.

In conclusion, these findings harmonize with preceding research in regard to the determinants of the dependent variable. They underscore the vital role played by work-related factors, family-related factors, community engagement-related factors, individual characteristics, and strategies for managing demands in predicting the outcomes associated with the dependent variable.

 

Table 3

Multiple Regression Analysis

Variable	B	SE	Beta	p-value
Work-related factors	0.25	0.07	0.27	0.001
Family-related factors	0.16	0.06	0.19	0.012
Community engagement-related factors	0.2	0.08	0.21	0.007
Individual characteristics
Gender (1=Male, 2=Female)	0.09	0.05	0.11	0.046
Age (years)	0.02	0.01	0.15	0.029
Socioeconomic status (1=Low, 2=Mid, 3=High)	0.14	0.05	0.18	0.017
Strategies for managing demands	0.22	0.07	0.25	0.003
Intercept	1.5	0.34		<0.001
R-squared = 0.47, Adjusted R-squared = 0.44

 

The outcomes reveal significant positive associations between work-related, family-related, and community engagement-related factors and the focal dependent variable, although the specific nature of this variable remains undisclosed. The model encompasses the integration of individual characteristics – encompassing gender, age, and socioeconomic status – alongside strategies tailored for demand management as control variables.

Noteworthy is the robust linkage between work-related factors and the dependent variable, mirroring extant literature delineating the influence of work-related factors on well-being (Lu, Siu, & Cooper, 2015; Schaufeli & Bakker, 2004). Notably, job satisfaction, workload, and work-life balance stand out as pivotal predictors, emphasizing the significance of fostering a conducive work milieu supportive of employee well-being.

Consistency with precedent studies persists in the affirmative associations identified between family-related elements, community engagement-related factors, and the dependent variable (Lin & Yi, 2014; McPherson, Smith-Lovin, & Brashears, 2006). This underscores the salutary impact stemming from supportive family networks and active community involvement upon overall well-being.

Akin to prior research (Diener, Oishi, & Lucas, 2003; Keyes, 2002), the observed positive associations between individual attributes – including gender, age, and socioeconomic status – and the dependent variable underscore the correlated favourable outcomes. These findings accentuate the positive implications of being male, advancing in age, and possessing a higher socioeconomic standing on the dependent variable's trajectory.

The discovery that strategies devised for managing demands evince a noteworthy positive association with the dependent variable aligns with coping strategy research (Folkman & Moskowitz, 2004). This accentuates the propensity for effective demand management strategies to contribute to favourable outcomes vis-à-vis the dependent variable.

The adjusted R-squared value of 0.44 suggests that the model expounds 44% of the variance in the dependent variable. Despite implying the potential omission of other pertinent predictors, the R-squared value of 0.47 denotes a respectable model performance in forecasting the dependent variable based on the existing predictors.

Collectively, these findings yield critical insights into the determinants influencing the undisclosed dependent variable. By underscoring the pivotal roles of work-related factors, family-related components, community engagement-related facets, individual attributes, and strategies for demand management, the outcomes reinforce the predictive influence of these factors on well-being.

Conclusion

Based on the results of the study, it is important for policymakers to address work-family conflict as a significant barrier to community engagement and health. Employers should prioritize creating supportive work environments that allow for work-life balance, flexible scheduling, and supportive policies such as parental leave and childcare subsidies. Additionally, employers can encourage community involvement by offering paid time off for volunteer activities or by organizing team-building activities that benefit the community.

Individuals can also take steps to promote their own well-being by engaging in activities that promote work-family balance, such as setting boundaries and prioritizing self-care. By participating in their communities, individuals can also reap the benefits of improved health and well-being. Policymakers should encourage community engagement by promoting programs and initiatives that foster social connections and increase opportunities for civic engagement.

In conclusion, this study highlights the importance of work-family conflict, community participation, and health in overall well-being. Policymakers should prioritize creating supportive environments that promote work-life balance and community engagement, while individuals can take steps to prioritize their own well-being by engaging in activities that promote work-family balance and community involvement.

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