URC

Rural Low Income Women’s Struggles and Strength: A Call to Change

Juliana Plumb, B.S. in Family Studies
*Bonnie Braun, Ph.D. Faculty Advisor
Department of Family Studies
University of Maryland, College Park


Abstract

Although many scholars have studied issues concerning rural poverty, few have investigated the unique way in which gender impacts rural culture and poverty. Using data from the Rural Families Speak research study, the author investigated the effect of a gendered perspective on views regarding rural, low-income women. The mixed-methods study sought to identify qualitatively the ways in which each woman’s role as partner, mother, and female citizen impacted their lives and to corroborate the findings with a quantitative analysis. The most significant factors emerging from the analysis included: employment, child support, childcare, food security, and emotional well-being.

Rural Low Income Women’s Struggles and Strength: A Call to Change

Introduction

Feminist scholars have long studied the ways in which women’s lives are negatively impacted by gender roles, discrimination, and oppression within modern America. Connell describes the overall process by which men benefit from their gender status within society as the “gendered accumulation process” (Connell, 2002). This accumulation process may refer to power, health, time, money, and wealth.

The differential in gender is especially problematic for women in lower socio-economic levels. Yet, while many studies of poverty analyze income disparities among American households, few address the inequalities that exist within them (Ruspini, 2002). Specifically, few studies analyze the ways in which men and women individually benefit or suffer from gender oppression as a key to understanding poverty. The literature on gender demonstrates that women can be negatively impacted by their gender and thus their well-being is diminished. Gender could be a factor for consideration in public policy and programs affecting the well-being of women and, if those women are mothers, the well-being of their children.

The phenomenon of gendered accumulation was the focus of this research study that sought to answer the general research question: Is there evidence of impact of the gendered accumulation process within a sample of women from the Rural Families Speak (RFS) study? The study reported here employed the theory of gendered accumulation as a lens through which to understand rural women's poverty. The analysis was done to reveal ways in which the roles of the rural women in this sample, as mother, partner, and citizen, may negatively affect their well-being (and thus contribute to the gendered accumulation process). The sample for this experimental study was limited to women in the Appalachian counties of the RFS study.

Method

In 1998, a team of researchers from 15 land-grant universities were authorized by the USDA-affiliated Agricultural Experiment Stations to conduct a unique, multi-state, longitudinal study known as Rural Families Speak to understand factors affecting the well-being of rural, low- income families. Beginning in 2000, data were collected from mothers age 18 and older, with at least one child under age 13 and incomes below 200% of poverty, eligible for or receiving food stamps, were recruited through programs serving low-income families. The counties from which the sample came were identified as meeting a rurality criteria of 6-8 based on the Butler and Beale (1994) coding scheme.

Sample

Data for this study were confined to Appalachian counties, a designated economically- depressed geographical region where forty-two percent of the population is rural; many live in persistent poverty and face multiple challenges (Dyk, Braun, Swanson, & Seiling, 2002). For this study, the sample used for quantitative analysis differed from the sample used for qualitative analysis. Data for the quantitative analysis were collected from 208 mothers in seven Appalachian counties of five states (Kentucky, Maryland, New York, Ohio and West Virginia). Data used for the qualitative analysis were gathered from the sub-sample of 11 Maryland mothers who were interviewed all three years. 

Data Collection and Analysis

Data were collected by on-site interviews using common instruments and open-ended questions over a period of three years. Standardized quantitative measures of physical and mental health, food security, employment, and income, including child support, were collected, coded, and made available for analysis using SPSS XI TM. Qualitative data were transcribed verbatim by the multi-state team. This researcher coded for thematic factors using the principles of grounded theory and qualitative analysis techniques (Berg, 2003).

This study employed an inductive, mixed-methods analysis. Qualitative analysis was employed first to identify, from the women’s own words, factors related to their well-being that provided evidence of negative impact by their relational roles. A search was conducted for factors that were mentioned by at least one-third of the participants (or 4 of the 11 women). Once thematic factors were identified, quotes were selected to capture the women’s own words as a means of exemplifying and explaining the phenomenon.

The quantitative analysis followed and complemented the qualitative analysis. Simple t-tests were run on all of the demographics, child support, child care subsidy, food security, and depressive symptoms screening data collected from all 208 participants from the seven Appalachian counties (See Table 1). A separate simple t-test was conducted on the 11 participants that were the Maryland sub-sample to compare the two on a variety of variables. For this article, only a few of the findings are included. For more specifics on others, see Plumb, (2006).

Results

Results are presented from both the qualitative and quantitative analysis. Conclusions are drawn on the combination of the two.

Qualitative

Five major thematic factors were extracted from the interviews that appeared to negatively impact the well-being of these mothers. The five identified factors were:

1. Child Support

Child support was defined as the financial support received or not received by mothers from their children’s’ father. Six of the 11 participants cited child support a total of 21 times during the interviews. Most reported that they had not received any support at all, while the others reported that payments were often erratic and/or not the full amount ordered through the court.

As one mother explained, “He hasn’t paid nothing. He did pay five hundred, but that was six or seven years ago.” Another mother recalled, “The last check was like 16 [dollars] or something. It’s nothing.” One mother explained that when she did receive a check, it would only complicate her life because it would disrupt her normal allowance for food stamps and other benefits. Then, the next month (when she did not receive a check) she was forced to reapply for the programs.

Some of the women have a much larger problem, however. They simply don’t know where the fathers of their children were. To aggravate the problem, they found the local authorities unreliable in helping them track down the fathers. One woman explained that although her ex-husband was finally caught for failing to pay child support, she would not receive the full amount they collected from him at the time of his arrest because she had hired a private agency in order to track him down. “But see, I went through a child support network, so when they get it, [owed child support] they get a third.

2. Childcare/Daycare/After school care

Childcare/Daycare/After school care was defined as the availability, quality, cost and type of care, and regulations surrounding care. Eight of the 11 participants cited the subject of care of children 25 times during their interviews.

The women spoke about their childcare situations, discussing the availability, quality, price, and logistics of the care in their area. Although most of the women were able to find childcare for their children at least some of the time, one explained her difficult situation with her young daughter, “There’s only certain people who will watch her because her seizures are deadly ones.”

The quality of care available was also a cause for concern among many of the women. “Having to be licensed doesn’t make them good providers.” More than one of the women shared horror stories they had heard about local providers abusing children. Others explained that some providers offered no structure and educational opportunities and simply let the children play all day.

Other mothers explained that the lack of after-school programs made it difficult to find care for their school-aged children. “There isn’t too many after school programs,” one mother explained. Another said outright that the schools her children attended had no after school care available at all, forcing her to either find a sitter or be home in the afternoons.

For some mothers, the issue was not finding quality care, but rather the hours and days they were able to get the care. “It’s more convenient to find a sitter for part-time,” one mother stated. This would no doubt make it very difficult to pursue salaried, full-time employment. Another mother explained that the work she was able to find in her area was mostly on the weekends, and that “It’s hard to find a sitter during that time.”

Not surprisingly, for many of the non-skilled mothers in the study, the cost of childcare made working outside the home financially impractical. If the only work available to the mothers is entry-level and/or minimum wage, practically all of their earnings (if not all) would go right to their care provider. One woman explained, “If I just went in a minimum wage job it wouldn’t be worth it [financially].”

3. Employment

Employment was defined as the mother’s ability to be employed, number of hours able to work, type of work available, and pay. Seven of the 11 participants cited it a total of 20 times during the interviews.

One common issue was women having to miss work because their children were sick and they were unable to find them childcare. The mothers explained that if their children had a fever, were throwing up, they were unable to leave them with their regular provider and were forced to stay home from their jobs. Another mother offered examples of when she had to suddenly leave work because her children were sick or hurt.

While most women did not state outright that they had been discriminated as mothers in the workplace, one mother speculated that the reason she didn’t get a job she had applied for was because the manager knew she had children. Still others spoke about the negative reactions their bosses had to their childcare and health emergencies.

4. Food Security

Food security was defined as the availability of food for the mother and her family, cost of food, and manner in which food was dispersed within the family. Four of the 11 participants cited being negatively impacted in terms of their food security 5 times.

“I know what it’s like not eat so my kids could.” This mother is not alone in her account of how food is distributed in her home. Many of the mothers admitted that, when money and food were tight, they were the first to go hungry [but only after asked directly). One mother admitted that she would not eat to “ease” her husband’s mind because he would be worried that they would not have enough to make it until they received next month’s food stamps. Another mother explained that, “Sometimes me and my husband would not eat lunch to make sure that the kids had lunch to eat on the weekends....” Yet another mother admitted to going whole days without eating anything at all in order to save food.

5. Emotional Well-being

Emotional well-being was defined as the mother’s state of being as related to stress, time for friends, time for enjoyable activities, and time alone Six of the 11 participants cited negative impacts to their emotional well-being 21 times.

Many of the mothers reported complicated mental health and well-being issues. Several made statements implying their low self-confidence and negative self-image was often directly linked to their partners. “[I told my husband that] if he didn’t straighten up I was going to go on a diet and get a boyfriend. But, he could care less… maybe if I lost the weight he might.” Another mother explained how her children respected her more now that they weren’t living with her husband, saying, “If he don’t care about anything I say or feel, why should they?”

More than one of the mothers recounted the horrific impact of domestic violence. “I went through a battered woman’s course, which I’m glad I did because I was really beaten down. I wouldn’t even get up and dress myself…I wouldn’t do nothing for myself.” Another mother explained the stress of running from an abusive ex-husband. “My ex-husband came looking for me, and I was terrified of him. He’s… I took some very bad beatings.”

Support for this research was provided by the United States Department of Agriculture (NRICGP2000-01759), American Association of Family and Consumer Sciences, Louisiana State University Agricultural Center, University of Maryland and University of Kentucky College of Agriculture. Data were collected in conjunction with the cooperative multi-state research project NC-223 Rural Low-Income Families: Monitoring Their Well-being and Functioning in the context of Welfare Reform. Cooperating states were: California, Colorado, Indiana, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Nebraska, New Hampshire, New York, Ohio, Oregon and Wyoming.

Quantitative

Simple t-tests confirmed that the all-Appalachian sample (Table 2) and the Maryland only sub-sample (Table 3) were similar in terms of the average number of children in the family, racial makeup, and annual income. The all-Appalachian sample had a higher rate of the participants having a partner, while the Maryland sample had a higher rate of employment than the larger sample. The average overall depression score and average food security score for the Maryland sample was also higher than that of the all-Appalachian sample.

The simple t-test run on the Maryland sub-sample data revealed important quantitative information to help explain the qualitative information previously collected and categorized from the same sub-sample interviews. These data, presented below, correspond to the five identified thematic factors.

1. Child Support

Although the quantitative data revealed that 83% of the eligible participants reported they were receiving some form of child or spousal support, this figure may be misleading. Closer examination of qualitative data revealed that while the majority of eligible women were “receiving support,” they were not likely receiving all of the child support they were owed for all of their children.

2. Childcare/After school care/Daycare

Another factor identified in the qualitative analysis was the issue of childcare/after school care/day care. Only one of the women had received childcare financial assistance. And while all of the women knew how to apply for welfare cash assistance, only 63.64% of the women knew how to apply for a childcare subsidy.

3. Employment

Many of the women (40%) were currently looking for work/more work. All of the 11 Maryland participants stated that they had worked for pay at some time in their lives. At the time of the study, 54.55% of the Maryland participants were employed, averaging 1.68 jobs, for an average total of 25.5 hours a week. This is in keeping with the statements that some of the women made about part-time work being easier for them.

4. Food Security

One of the less frequent but still identified factors was the issue of food security. The statistical analysis corroborated the statements made by many of the women. No less than 80% of the women reported that they ate less in order to make food stretch in their homes. While 40% reported they had gone hungry.

5. Emotional Well-being

The Maryland sample exhibited high rates of depressive symptoms related directly to emotional well-being. Sixty-three percent of the participants stated they had experienced depression and 36.36% had an eating disorder or were obese.

Discussion

More than anything, the study has enabled the words of the mothers to answer the question of whether or not there is evidence of a gendered accumulation process in the RFS study. The women frequently, and with some consistency, spoke to the ways in which their roles as mother, partner, and citizen influenced their lives. They discussed the ways that non-existent and erratic child support payments left them financially unstable and without state benefits. They explained how quality childcare was often difficult to secure for their children, and how the high cost and inflexible hours of childcare makes employment difficult for them. The mothers also spoke about their food security; often admitting that they would sacrifice their own food intake to make sure their family had enough. Finally, they talked about their emotional well-being; admitting to high levels of stress and suffering from psychological disorders. And, while not all of the quantitative data from the Maryland sub-sample, as analyzed, was able to corroborate and better explain the qualitative findings, several quantitative factors did reinforce the mothers’ statements.

For example, while 83% of the mother’s reported they received child support, the qualitative statements seemed to suggest otherwise. Upon closer examination, the quantitative evidence showed that perhaps the mothers were receiving child support, but not necessarily for all their children. The qualitative findings suggested that many mothers were burdened by the high cost and lack of flexibility of childcare, yet only 9% of them reported receiving childcare financial subsidizes. The average number of hours worked per week (only 25) was able to substantiate the statements made by many of the mothers that only part-time work was available to them. In keeping with the statements many mother’s made about stretching the food budget, 80% reported they had eaten less in order to make sure their families could eat. Confirming the statements by the participants regarding their lack of friends and stressful financial situations, 63% percent of the participants reported coping with depression and anxiety.

The mixed-methods approach enabled the qualitative data to explain and support the quantitative data, and vice versa. The quantitative statistics corroborated qualitative findings and offered a better understanding of the extent to which women may be experiencing conditions about which only a few explicitly spoke. Conversely, the qualitative findings helped to give explanations and a deeper meaning to the quantitative data. The mixed-methods design afforded a greater sense of understanding and accuracy to the lived conditions of these women.

As Aslanbeigui, Pressman, & Summerfield, (2003) discussed, gender equity can be addressed in the public policy arena. The findings from this study provide policy implications for those interested in better serving the needs of rural, low-income women. Policy suggestions related to the five thematic factors follow:

  • Child Support : Although most of the women received some child support, many were not receiving all they were owed. State authorities need to work together to track down and collect from fathers who are not paying their court-ordered child support. Creating better record networks and extradition laws would help to better secure payments.
  • Child Care: Many of the mothers spoke the difficulty in finding quality and affordable childcare/daycare. Many others complained about their local school system’s lack of after-school care. Making sure that registered childcare facilities meet high standards of care, creating comprehensive childcare subsidizes, and notifying women of their availability should support rural women in seeking and maintaining employment. Provision of after-school programs could also support mothers’ employment.
  • Employment: More than one of the mothers testified that the prohibitive cost of childcare kept them from working. By making childcare/ daycare more affordable and accessible, mothers should be better able to work. Some explained how often their employers were unsympathetic when they were forced to leave work due to an emergency with their children. Educating employers to be more family-friendly and workers to stand up for their legal rights should create a better working environment for mothers.
  • Food Security: Many of the women cut back on their food intake to ensure their children and partners would have enough. By supporting food security screening, food cooperatives, WIC programs, community/home gardens, and food banks, families could become more food secure. Encouraging use of food stamps and enrolling families in nutrition education programs as well as expanding the availability of, and access to, affordable food could further reduce food insecurity. If families are food secure, then mothers will no longer endanger their own health to feed their families.
  • Emotional Well-Being: Many of the women complained of little or no time to themselves or with friends. Encouraging mothers to make time for themselves and their friends (and providing them with the childcare) may help mothers decrease the stress and avoid more serious mental health problems like depression and anxiety. More than one woman explained a history of physical abuse. Continuing to support domestic violence shelters and affordable therapy for victims should help these women be happier and healthier.

These, and other policy implications drawn from the wealth of information gathered, may be practically and financially difficult, yet they offer real solutions for better serving rural, low-income women. By taking a gendered perspective, the mothers’ own words can lead to solutions that target their often-ignored needs.

Ultimately, based on the qualitative and quantitative results, there is undoubtedly evidence that the mothers studied in the RFS sample were negatively impacted due to their relational roles. The results explain ways in which these women experience poverty. Further qualitative research on the Appalachian women, and ultimately on all women in the RFS study, would refute or confirm these exploratory findings.

The Maryland mothers shared stories of their personal sacrifices and familial struggles, as well as their isolation from resources and information. But they also exhibited drive to survive and succeed within their communities and families. It is both their struggles and their strength that inspires a call to change.

References

Aslanbeigui, N., Pressman, S. & Summerfield, G. (2003). Toward gender equity: policies and strategies. International Journal of Politics, Culture & Society, 16(3) 327.

Berg, B. L. (2003). Qualitative research methods for the social sciences (5th ed.). Boston: Allyn and Bacon.

Butler, M.A. and Beale, C.L. 1994. Rural-Urban continuum codes of metro and non- metrocounties, 1993. (Staff Report No. 9425), Agriculture and Rural Economy Division, Economic Research Service, Washington, DC: USDA.

Connell, R.W. (2002). Gender. Blackwell: Malden.

Dyk, P. H., Braun, B., Swanson, J. & Seiling, S. (2002). Appalachian rural family economic well-being in the context of public assistance. Presentationat the annual meeting of the Rural Sociological Society, August 14-18, 2002, Chicago IL.

Plumb, J. (2006). Rural Low-Income Women: Taking a Gendered Perspective. Undergraduate Honors Thesis. University of Maryland: College, Park.

Ruspini, E. (2002). The study of women’s deprivation: How to reveal the gender dimension of poverty. International Journal of Social Research Methodology, 4(2).

Table 1. Appalachian Sample & Maryland Sub-Sample

 

Maryland Sub-Sample

All-Appalachian Sample

Avg. Age

29.54 years

28 years

Racial Makeup

Non-Hispanic White: 89.4%

Hispanic/Latino: .5%

African American: 6.7%

Native American: 1.9%

Multi-racial: 1.4%

Non-Hispanic White: 93.9%

Hispanic/Latino: 0%

African American: 0%

Native American: .6%

Multi-racial: 0%

Participant has partner

45.4%

63%

Avg. No. of Children

2.36

2.2

Employed Currently

54.55%

44%

Avg. Household Income

$12,545

$13,311

Avg. Knowledge of Community Services Score

19.18

18.33

Avg. depression score

23.18

19.47

Avg. Life Skill Assessment score

19.45

19.09

Avg. food security score

5.36

3.49

Amount of stress right now

1.0

1.786

Avg. parental confidence score:

28.27

31.08

 

Table 2. All Appalachian Sample T-Test Continued

 

Test Value = 0

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

Participant's age at the time of interview

57.661

205

.000

28.21359

27.2489

29.1783

Does a participant have a partner?

18.766

207

.000

.62981

.5636

.6960

Partner's age at the time of interview

41.406

127

.000

32.82813

31.2593

34.3970

Number of children

27.166

207

.000

2.22115

2.0600

2.3823

Self-currently working?

12.813

207

.000

.44231

.3743

.5104

Self - currently looking for work?

7.815

113

.000

.35088

.2619

.4398

Self - ever worked for pay?

69.599

173

.000

.96552

.9381

.9929

Self - seasonal worker?

2.897

150

.004

.05298

.0168

.0891

Self - number of jobs

21.742

89

.000

1.20000

1.0903

1.3097

Self - Total hours worked per week

24.096

72

.000

29.61644

27.1663

32.0666

Partner - currently working?

17.672

129

.000

.70769

.6285

.7869

Partner - seasonal worker?

2.716

117

.008

.05932

.0161

.1026

Partner - number of jobs

29.968

91

.000

1.11957

1.0454

1.1938

Partner - Total hours worked per week

32.328

80

.000

44.16049

41.4420

46.8790

Self - number of childcare arrangements

11.605

204

.000

1.09756

.9111

1.2840

Self - Payment of childcare arrangement 1

25.861

112

.000

2.53982

2.3452

2.7344

Total annual income (not included food stamp)

19.403

207

.000

13310.67231

11958.2010

14663.1436

TANF ( Temporary assistance to needy families)/MFIP(Minnesota)

5.460

207

.000

46.20471

29.5203

62.8892

Child or spousal support

5.490

159

.000

73.14438

46.8319

99.4568

Food Stamps

14.824

204

.000

161.10244

139.6746

182.5303

School Lunch Program

17.089

202

.000

.59113

.5229

.6593

EIC(Earned Income Credit)

16.205

206

.000

.56039

.4922

.6286

Child care assistance

6.135

207

.000

.15385

.1044

.2033

Total scores of knowledge of community services

62.666

166

.000

18.33533

17.7577

18.9130

Participant - Depression/Anxiety

12.200

207

.000

.41827

.3507

.4859

Participant - Anger management

4.821

207

.000

.10096

.0597

.1422

Participant - Fatigue

9.700

207

.000

.31250

.2490

.3760

Participant - Eating disorder/obesity

7.237

207

.000

.20192

.1469

.2569

Participant - Emotional, physical, or sexual abuse

5.675

207

.000

.13462

.0878

.1814

Participant - Migraines/Headaches

13.192

207

.000

.45673

.3885

.5250

Cut the size of meal

11.437

109

.000

.54545

.4509

.6400

Eat less

10.110

108

.000

.48624

.3909

.5816

Hungry

6.563

107

.000

.28704

.2003

.3737

Not eat for a whole day

5.680

66

.000

.32836

.2129

.4438

How often not eat for a whole day?

7.071

20

.000

.71429

.5036

.9250

Food Security Score

11.993

172

.000

3.48555

2.9119

4.0592

Confidence that you know what is right for kid

67.763

200

.000

5.12438

4.9753

5.2735

Amount of stress right now

14.525

200

.000

1.78607

1.5436

2.0285

Ability to cope with stress

45.783

200

.000

4.48756

4.2943

4.6808

Total scores of parental confidence

89.258

198

.000

31.07538

30.3888

31.7619

Total scores of parental support

49.760

198

.000

26.83920

25.7755

27.9028

Table 3. Maryland Sub-Sample T-Test

 

Test Value = 0

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

Lower

Upper

Participant's age at the time of interview

14.828

10

.000

29.54545

25.1058

33.9851

Does a participant have a partner?

2.887

10

.016

.45455

.1037

.8054

Partner's age at the time of interview

19.453

4

.000

32.20000

27.6042

36.7958

Number of children

8.480

10

.000

2.36364

1.7426

2.9847

Total number of family members

15.872

10

.000

4.18182

3.5947

4.7689

Self-currently working?

3.464

10

.006

.54545

.1946

.8963

Self - number of jobs

3.953

5

.011

1.66667

.5828

2.7505

Self - Total hours worked per week

5.251

3

.013

25.50000

10.0452

40.9548

Self - educational level

9.960

10

.000

3.36364

2.6112

4.1161

Total annual income (not included food stamp)

6.369

10

.000

12545.45455

8156.4946

16934.4145

Child or spousal support

2.452

5

.058

83.00000

-4.0209

170.0209

Food Stamps

4.138

10

.002

193.36364

89.2470

297.4803

School Lunch Program

6.708

10

.000

.81818

.5464

1.0899

Child care assistance

1.000

10

.341

.09091

-.1116

.2935

Total scores of knowledge of community services

13.162

10

.000

19.18182

15.9346

22.4291

Total scores of depression scales

6.064

10

.000

23.18182

14.6638

31.6999

Total scores of life skill assessment

18.886

10

.000

19.45455

17.1593

21.7498

Participant - High blood pressure

1.491

10

.167

.18182

-.0899

.4536

Participant - Depression/Anxiety

4.183

10

.002

.63636

.2974

.9753

Participant - Anger management

1.491

10

.167

.18182

-.0899

.4536

Participant - Fatigue

2.390

10

.038

.36364

.0247

.7026

Participant - Eating disorder/obesity

2.390

10

.038

.36364

.0247

.7026

Participant - Migraines/Headaches

2.887

10

.016

.45455

.1037

.8054

How often cut the size of meal?

2.121

6

.078

.42857

-.0658

.9229

Eat less

6.000

9

.000

.80000

.4984

1.1016

Hungry

2.449

9

.037

.40000

.0306

.7694

 

Test Value = 0

6

.172

.28571

-.1656

.7370

 

t

df

Sig. (2-tailed)

Mean Difference

95% Confidence Interval of the Difference

6.8531

         

Lower

Upper

Confidence that you know what is right for kid

19.333

10

.000

5.27273

4.6651

5.8804

Ability to create safe home for kid

14.330

10

.000

4.90909

4.1458

5.6724

Success in teaching kid to behave

9.113

10

.000

4.27273

3.2280

5.3174

Find fun activities of interest to kid

11.423

10

.000

4.45455

3.5857

5.3234

Amount of stress right now

3.708

10

.004

1.00000

.3991

1.6009

Ability to cope with stress

7.262

10

.000

4.09091

2.8357

5.3461

Total scores of parental confidence

17.288

10

.000

28.27273

24.6289

31.9165

Total scores of parental support

6.203

10

.000

23.45455

15.0295

31.8796


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