Perceived Quality of Life of Single Mothers
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Perceived Quality of Life of Single Mothers Living in Affordable Housing in Columbus, OH Shantha Balaswamy, PhD, [email protected] Nicole Carbonari, BSSW Undergraduate, [email protected] Abstract. Results. Implications.

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Perceived Quality of Life of Single Mothers

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Perceived quality of life of single mothers

Perceived Quality of Life of Single Mothers

Living in Affordable Housing in Columbus, OH Shantha Balaswamy, PhD, [email protected]

Nicole Carbonari, BSSW Undergraduate, [email protected]

Abstract

Results

Implications

Quality of life (QOL) frameworks have been used in various human service fields to help facilitate development of policies and programs with goals to improve lives of individuals and communities. However, only few studies have addressed the QOL of low-income single mothers who have considerable challenges negotiating their work life and family life. Studies indicate that the QOL of single mothers varies based on their personal characteristics like their age, education level, employment status and age of their youngest child. Other factors such as length of time spent in public housing, apartment condition, and neighborhood characteristics also impact their QOL. While literature suggest single mothers have higher levels of stress, little knowledge exists about its influence on their physical health, mental health and the effect of social support. The QOL is a multi-dimensional concept and includes variables like health condition, mental health status, economic stress, social support and general life satisfaction. This study explores the relationship between QOL indicators found in previous studies. A sample of single mothers was randomly selected from a subsidized housing community owned by Homeport called Marsh Run in Columbus, OH. Participants completed either a self-administered survey or in-person interview using a structured questionnaire to accommodate literacy levels. The questionnaire included information on demographics, health and mental health conditions, psychosocial measures, and economic and environmental factors. The findings indicate that the mother’s mental health is strongly correlated to overall life satisfaction- a proxy measure for QOL. It confirms that mothers who reported higher education levels also stated having more perceived social support and higher levels of overall life satisfaction. These findings underscore the importance of social support in reducing stress and improving the QOL. Findings also have implications for practice and policies for nonprofit sectors.

Table 2. Correlation between Demographic Variables and General Life Satisfaction

Table 1. Demographic Characteristics of Sample (n=21)

  • Mental health issues are directly related to general life satisfaction and perceived social support. Those who do not have social support are more vulnerable, therefore, conducting mental health screenings and linking residents to outreach services by Homeport can improve QOL for single mothers.

  • Recommend Homeport implement stress-management training and services to improve the mental health status of single mother residents.

  • There is a need for improving social support among single mother residents. Recommend Homeport make community building a priority at Marsh Run by holding community conversations and Community Leadership Institutes to engage residents in communal activities. Also, designating a community organizer for Marsh Run would be beneficial.

  • Recommend improving access to educational resources to advance education levels of residents, in order to help improve their potential in increasing economic and social power which is directly related to QOL.

  • Existing literature and this study’s findings suggest social policies should focus on improving recipients QOL. Including improved QOL as a desired outcome for social programs would potentially lead to better mental and physical health and economic condition consequently decreasing reliance on social programs.

Objectives

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  • To analyze the perceived quality of life of low-income single mothers living in affordable housing.

  • To determine which demographic variables (such as, age, race, number of children, education, income, etc.) are correlated to quality of life of single mothers.

  • To determine which psychosocial, economic and environmental variables (housing condition, mental health, physical health, social support, and financial strain) are correlated to overall life satisfaction.

Table 4. Correlation between Environmental Variables and General Life Satisfaction

Table 3. Correlation between Psychosocial and Economic Variables and General Life Satisfaction

Limitations & Future Research

  • Obtaining adequate sample size was the biggest challenge.

  • There was a discrepancy between the actual number of single mothers interviewed (n= 21) and the projected sample size (n= 50) of the study.

  • Data collection by student was not conducive to gaining access to respondents in a timely manner. Having co-interviewers or starting interviews earlier would have improved the chances of obtaining more participants

  • Due to general mistrust of system and lack of socialization in research surveys led to lower participation. Researcher should spend more time in the community to gain the participants confidence prior to collecting data.

  • Both sample size and cross-sectional design contribute to limited generalizability of the findings and limited statistical analysis methods that can be used.

  • Future studies should include measures like hassle scale which is a better measure of daily stressors, relationship with neighbors, reasons for unemployment, physical measures etc.

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Methodology

  • The sample of single mothers was taken from Marsh Run, an affordable housing community owned by Homeport. Marsh Run is comprised of 184 two- three- and four- bedroom apartments in southeast Columbus. As of January 2013, 167 units in Marsh Run were occupied and approximately 80 percent of those were occupied by single mothers.

  • A sample of 21/100 mothers were randomly selected to participate in the study.

  • Data was collected from January - February, through a combination of face to face and self-administered questionnaires. Surveys took 15-20 minutes to complete.

  • The survey comprised of demographics and indicators of quality of life like including psychosocial, economic, environmental and life-satisfaction.

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Findings

  • No correlation between demographic variables and general life satisfaction were found to be significant with the exception of education level (r = .476, p<.05) and the number of people permanently living in the home (r = .471, p< .05). Participants with higher education levels rated their general life satisfaction better than those with lower education levels. Similarly, participants who had more support at home, rated their general life satisfaction higher than those with fewer household members.

  • General life satisfaction was found to be highly correlated with the three measures of mental health, i.e., stress (r = -.95, p<.01), number of days mental health was not good in the past 30 days (r = -.680, p<.001) and number of days mental health prevented daily routine in the past 30 days (r = -.624, p< .01). The higher a participant perceived having mental health problems the lower they rated their general life satisfaction.

References

Figure 1. Quality of Life Measures

  • Hollar, D. (2003). A holistic theoretical model for examining welfare reform: Quality of life. Public Administration Review 63:90-104.

  • Cook, K., Davis, E., Smyth, P. & McKenzie, P. (2009). The quality of life of single mothers making the transition from welfare to work. Women and Health 49:6-7, 475-490.

Environmental Factors

-H1 Infestation

-H2 Neighborhood

-H3 Condition

Financial Strain

  • The relationship between general life satisfaction and the environmental variable, housing condition was statistically significant (r = -.452, p<.05). Women who rated their housing condition to be good rated their general life satisfaction to be higher.

  • The amount of perceived social support seems to be highly correlated to being depressed or stressed out (r = -.564, p<.01), number of days mental health was not good in the past 30 days (r = -.591, p<.01), number of days mental health prevented daily routine in the past 30 days (r = -.706, p<.001) and financial strain (r = .465, p<.05). The lower a participant rated their social support, the higher the reporting of mental health issues and financial strain.

  • A significant correlation existed between social support and subjective wellbeing (r = -.465, p<.05), number of days physical health was not good in the past 30 days (r = -.482, p<.05) and number of days physical health prevented daily routine in the past 30 days (r = -.544, p<.05). The lower a participant rated their social support the higher their physical health problems.

Demographics

-Age, race, income ,education, employment

-# of children, age of youngest, # of children in the home

-Section 8 voucher, LOT at Marsh, LOT in affordable housing, # of bedrooms

Life Satisfaction

Acknowledgements

Mental Health

- Level of stress

  • # of days mental health was not good in the past 30 days

  • # of days mental health prevented daily routine in past 30 days.

Physical Health

-Subjective well-being

-# of days physical health was not good in the past 30 days

- # of days physical health prevented daily routine in past 30 days

I would like to thank Homeport for their support and encouragement throughout the entire research process, especially, Gywn Stetler, Alex Romstedt and Amy Klaben. I would also like to thank Jennie Babcock and the College of Social Work for their continual support and commitment to advancing social work research and education.

Social Support


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