The mediating role of religiosity and hope for the effect of self-stigma on psychological well-being among COVID-19 patients

1.1Religiosity and psychological well-being

Major life crises affect people psychologically, socially, and physically, as well as religiously and spiritually. There is no doubt that religiosity affects the psychological well-being and mental health, which affects the physical health of individuals and their ability to cope with epidemic infectious diseases, such as COVID-19 [8].

Despite the many writings on the importance of evaluation and intervention related to the patient’s religious needs, there is as yet little understanding of religiosity’s effect on the care provided by health personnel to patients [9]. The number of papers studying the relationship between religion and health in psychosocial and behavioral sciences has increased significantly [10].

The previous studies in this area showed the role of religion in health consequences. Religiosity correlates with health-related physiological processes such as cardiovascular, neuroendocrine, and immune function [11]. Regular religious attendance is related to a wide range of healthy behaviors, such as preventive care use, vitamin use, infrequent bar attendance, seat belt use, walking, strenuous exercise, sound sleep quality, never smoking, and moderate drinking [12]. Daily spiritual experiences were related to more significant health behaviors, while religion was less related to this [13]. Religiosity as a predictor of breast cancers’ psychological well-being [14].

1.2Hope and psychological well-being

The current century is considered the era of positive psychology and the study of positive features in an individual’s personality [15]. Hope is one of the essential concepts in this field; it can promote individuals’ psychological health. Hope an individual’s performance future or an individual’s competencies to achieve aims [16]. Instead, hope is the components for future expectations, and these components are based mainly on how an individual understands the world, instead of assessing their competencies [16–18].

By reviewing the theoretical literature on hope, we found that Seligman mentioned that hope is a critical component of a person’s everyday life. He stressed the importance of the relationship between hope and psychological well-being. He also saw that hope belongs to the positive emotions that have a relationship with the individual’s future, and these emotions are one of the necessary components of happiness and psychological well-being. Therefore, hope is a living experience that means a person who enjoys this experience moves towards the future. His efforts mobilized to transcend the present and contribute effectively to making a better future. That is why the theory of hope assumes that those with high hope can form new paths to their desired aims when facing obstacles and stress in their lives.

From the above, we noted that hope is significant for all individuals who face stresses, such as patients, to adapt to their disease. Several studies have found that hope has strong positive relations with various psychological outcomes, such as emotional adjustment and quality of life [19]. Foote et al. [20] indicated a relationship was found between hope, social support, and self-esteem in patients. In the same context, patients with high hope showed high acceptance of the disease and psychological pain [15]. Change [21] found a relationship was found between hope and quality of life for university students. Also, the cancer patients’ hope was a predictor of adapting and accepting the disease [22]. About the relation between hope and quality of life, Brooks et al. [23] found between hope and quality of life in elderly cancer patients. The previous studies detected the mediating role of hope in alleviating the impact of HIV stress on the quality of their lives [24, 25]. Hope is a statistical predictor of the concept of self and quality of life among patients with kidney failure [6].

Recently, study about the role of hope in shaping psychological health and psychological well-being during COVID-19, recommended to pay more attention to hope for improving psychological health during crisis time [26]. Another study indicates that the intense level of hope correlates with a lower level of anxiety and lower COVID-19 stress. Hope acts as a buffer on the face of the anxiety and the stress reactions on the virus pandemic [18]. Sheretta et al. [24] outlined the potent role of the psychological risk that could arise from the lockdown or physical distancing of COVID-19. Providing psychological skills increases hopefulness during this challenging time, Bloch-Atefi et al. [27] reported that give the clients with positive expectation and help them construct hope that makes sense for them. That could be enormously helpful in making this difficult time more manageable.

1.3Self-stigma and psychological well-being

Self-stigma is a subjective process characterized by negative feelings towards the self, non-adaptive behavior, a shift in identity, and results from an indi-vidual’s experiences or perceptions or anticipating negative social reactions [28]. Self-stigma is a concept that refers to a set of countermeasures, stereo-types, discriminatory behaviors, and biased social groupings endorsed by a large group towards ano-ther subgroup. The COVID-19 patients’ self-stigma occurs when members of a stigmatized group incorporate negative views of themselves due to their sense of humiliation and isolation from others. Stigma is a mark of disgrace that distinguishes a person from others [29]. It has many negative effects that prevent healing the disease, such as feeling shame and embarrassment [30, 31].

There are three types of stigma: Public stigma: It is the most well-known species, and some call it t group’s stigma. The focus is on the general attitudes and the attitudes of society towards persons subject to stigma. Self-stigma: The focus on this type is about assimilating and integrating individuals with negative societal views. Family stigma: This type refers to the stigma experienced by the individual due to his association with relatives who suffer stigma [30]. Brohan [7] divided stigma into three types: Extreme hatred of the body: as in the case of physical disability or apparent deformity, for example. Defects of an individual nature: such as mental illness or criminal conviction. Tribe stigma: includes race, gender, and age.

Self-stigma is associated with an increased rate of mental problems, anxiety, depression, and poor health in general [30]. Self-stigma hurts individuals, as it leads to a decrease in self-esteem, self-efficacy, life satisfaction, social adjustment, overall psychological well-being, and social contact [32]. Stigma is associated with fear of social discrimination and often chases certain people or groups because of its association with the diseases’ place and time. Stigma is a reason for isolating stigmatized persons and their fear of exposure to past or racial discrimination. Studies have shown that stigma and a sense of shame negatively affect the progress of treatment and medical intervention with patients, including COVID-19 patients, as happened previously with other patients such as HIV [33]. According to UNICEF [34], “Social stigma in the context of health is the negative association between a person or group of people who share certain characteristics and a specific disease. In an outbreak, this may mean people are label typed, discriminated against, treated separately, and experience loss of status because of a perceived link with a disease”. The COVID-19 outbreak resulted in social stigma and discriminatory behaviors against certain races and anyone infected with the virus.

Thus, we can define COVID-19 patients’ self-stigma as a perceived negative trait of COVID-19 patients to negative views and beliefs that would adversely affect their behaviors include: awareness of social discrimination, the incorporation of stigma, and avoiding situations capable of provoking him. Breast cancer self-stigma results in a lack of self-efficacy and social skills, with a loss of the original identity and its replacement with a new socially unacceptable identity that isolates them from society and strengthens the idea of social rejection, as well as a feeling of psychological insufficiency.

The degree of stigma associated with COVID-19 disease depends on some factors:(1) It is a new disease, and there is still a lot of new information; (2) We are often afraid of the unknown; (3) It is easy to link this fear to others.” Understandably, there is confusion, anxiety, and fear among the audience. Unfortunately, these factors fuel harmful stereotypes [34–36]. Also, studies found that the quarantine and strict tracking of infected cases by health authorities may cause society to reject these measures, and the spread of discrimination and social stigma among people [24]. Studies have shown that during an outbreak of epidemic diseases, which put severe stress on public health services, some people who are more likely to be at risk of stigma and discrimination will need special medical concerns [13, 37]. During the SARS virus, some people became fearful and suspicious of all individuals who seemed Asian, regardless of their nationality or the real risk factors for the disease [13].

Persons who are feared and stigmatized may delay seeking care and remain in the community undetected”. Person et al. [13] found that discrimination often has social and economic ramifications that intensify internalized stigmatization and feelings of fear. In the same, stigma affected most residents and took various forms of being shunned, insulted, marginalized, and rejected in work, interpersonal relationships, use of services, and schooling. Lee et al. [36] indicated that Stigma was also associated with psychosomatic distress. James et al. [38] found a high level of internalized stigma (0.92±0.77) Compared to total enacted stigma that came relatively low (0.71±0.61) among Ebola survivors in Sierra Leone. Religiosity perceived health status and region were the independent predictors of stigma. After checking the results of many studies [39–41] about stigma among patients, we can summarize these results as follows:

These adverse effects that self-stigma has on COVID-19 patients’ personalities may disrupt their awareness of reality, distort their way of thinking, and attack them many psychological disorders.

1.4COVID-19 patients’ psychological well-being

The Kingdom of Saudi Arabia is considered the Arab country in which COVID-19 has spread most, with estimates that reached in May 20-2020 to approximately 154,233 cases, of which 98,917 recovered, while the total mortality of the virus reached 1230 patients [42] confirmed cases of the COVID-19 might experience fear of severe disease consequences [36, 43]. As a result, their PWB is affected because they may suffer from anxiety, insomnia, denial, loneliness, and depression, reducing their benefit from COVID’s medical treatment. Also, some of these conditions may increase the risk of suicide. Furthermore, isolated cases may suffer from anxiety due to uncertainty about their health condition and the development of Obsessive Compulsive disorder symptoms, such as temperature checks and frequent sterilization [36].

3.1Participants

A random sample of 426 COVID-19 patients who reside in the Kingdom of Saudi Arabian. The researchers choose the study sample from different ages, social statuses, education levels, and income levels. A total of 11.0%(N = 47) were less than 30 years old, 61.2%(N = 262) between 30–50 years old, and 27.5%(N = 117) were over than 50 years old. While the distribution of the sample at the level of education in three levels, a total of 28.9%(N = 123) less than university education level, 59.6%(N = 254) university graduates, and 11.5%(N = 49) were postgraduate. Out of 79.1%(N = 337) were married, 14.1%(N = 60) was unmarried, 4.9%(N = 21) were divorced, and 1.9%(N = 8) widower. This study sample was distributed over four income levels, a total of 44.6 (N = 190) less than 10 thousand SAR, 44.1%(N = 188) between 10–20 thousand SAR, 8.0%(N = 34) between 20–30 thousand SAR, and 3.3%(N = 14) more than 30 thousand SAR.

3.2Procedures

The current study applied a descriptive method to detect the differences in religiosity, hope, stigma, and psychological well-being and reveal the direct and indirect effects among religiosity, hope, stigma, and well-being. The online questionnaire (contain self-report scales of religiosity, hope, stigma, and psychological well-being) sent to the study sample.

3.3Instruments

3.3.1COVID-19 Patients’ Religiosity Scale (CPRS-9)

In this study, the researchers prepared the self- report COVID-19 Patients’ Religiosity Scale, which consisted of 9 items. The participate response with a 5-point Likert scale (full agree = 5 to not fully agree = 1). The correlations between CPRS-9 and the scale’s total score were calculated, and the correlation coefficients ranged from 0.400 to 0.813 and were statistically significant at 0.01 level (2-tailed). Also, Cronbach’s Alpha for the CPRS-9 was 0.761, Spearman-Brown Coefficient for unequal length was 0.688. These results indicated that CPRS-9 is validated and reliable.

Exploratory Factor Analysis (EFA) the Principle Components Analysis (PCA) method was used to derive CPRS-9 factors. EFA produced two factors that accounted for (62.283%) of the total variance of the scale. The results are shown in Table 1.

Table 1

Rotated Component Matrix^a

	Component
	1	2
R1		0.791
R2	0.877
R3		0.733
R4		0.735
R5		0.776
R6	0.684
R7	0.598
R8	0.729
R9	0.739
Total of variance	34.030	28.253

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

To validate the religiosity factors, Confirmatory Factor Analysis CFA was used. The results showed that there are appropriate goodness fit indexes (CMIN/DF = 3.652; NFI = 0.808; CFI = 0.849; RMSEA = 0.05), all of which indicated that the religiosity standards proposed model is acceptable. All of the religiosity observed variables had statistically significant (p < 0.001) loadings on the respective latent variables (see Fig. 1).

Fig. 1

Confirmatory factor analysis of the religiosity standards proposed model.

3.3.2COVID-19 Patients’ Hope Scale (CPHS-8)

The researchers prepared the COVID-19 Patients’ Hope Scale, which consisted of 8 items. The participate responds with a 5-point Likert scale (fully agree = 5 to not fully agree = 1). The correlations between CPHS-8 and the total scale score were calculated. The correlation coefficients ranged between 0.429 and 0.659, with significance at the 0.01 level (2-tailed). As well as, Cronbach’s Alpha for the CPHS-8 was 0.639, Spearman-Brown Coefficient for unequal length was 0.430. These results indicated that CPHS-8 is validated and reliable.

Exploratory Factor Analysis (EFA) the Principle Components Analysis (PCA) method was used to derive CSHS-8 factors. EFA produced two factors that accounted for (53.886%) of the total variance of the scale. The results are shown in Table 2.

Table 2

Rotated Component Matrix^a

	Component
	1	2
H1	0.478
H2		0.734
H3		0.747
H4		0.549
H5	0.722
H6	0.726
H7	0.895
H8	0.874
Total of variance	36.120	17.766

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

To validate the hope factors, Confirmatory Factor Analysis CFA was used. The results showed that there are appropriate goodness fit indexes (CMIN/DF = 2.360; NFI = 0.860; CFI = 0.878; RMSEA =0.053), all of which indicated that the hope standards proposed model is acceptable. All of the Hope observed variables had statistically significant (p < 0.001) loadings on the respective latent variables (see Fig. 2).

Fig. 2

Confirmatory factor analysis of the hope standards proposed model.

3.3.3COVID-19 Patients’ Stigma Scale (CPSS-10)

The self- report COVID-19 Patients’ Stigma Scale, which consisted of 10 items. The participate responds with a 5-point Likert scale (fully agree = 5 to not fully agree = 1). The correlations between CPSS-10 and the scale’s total score were calculated, and the correlation coefficients ranged from 0.526 to 0.804, with significance at 0.01 level (2-tailed). As well as, Cronbach’s Alpha for the CPSS-10 was 0.884, Spearman-Brown Coefficient for unequal length was 0.881. These results indicated that the CPSS-10 is validated and reliable.

Exploratory Factor Analysis (EFA) the Principle Components Analysis (PCA) method was used to derive CPSS-10 factors. EFA produced two factors that accounted for (65.743%) of the total variance of the scale. The results are shown in Table 3.

Table 3

Rotated Component Matrix^a

	Component
	1	2
S1		0.804
S2		0.643
S3	0.776
S4	0.841
S5	0.801
S6	0.818
S7	0.806
S8	0.663
S9		0.754
S10		0.546
Total of variance	42.851	22.892

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Confirmatory Factor Analysis CFA was used to validate the stigma factors. The results showed that there are appropriate goodness fit indexes (CMIN/DF = 3.281; NFI = 0.789; CFI = 0.832; RMSEA =0.049) that the stigma standards proposed model is acceptable. All of the Hope observed variables had statistically significant (p < 0.001) loadings on the respective latent variables (see Fig. 3).

Fig. 3

Confirmatory factor analysis of the stigma standards proposed model.

3.3.4COVID-19 Patients’ Psychological Well-being Scale (CPWS-10)

The researchers prepared the self-report COVID-19 Patients’ Psychological Well-being Scale, which consisted of 10 items. The participate responds with a 5-point Likert scale (fully agree = 5 to not fully agree = 1). The correlations between CPWS-10 and the scale’s total score were calculated, and the correlation coefficients ranged from 0.524 to 0.718, with significance at 0.01 level (2-tailed). As well as, Cronbach’s Alpha for the CPWS-10 was 0.809, Spearman-Brown Coefficient for unequal length was 0.790. These results indicated that CPWS-10 is validated and reliable.

Exploratory Factor Analysis (EFA) the Principle Components Analysis (PCA) method was used to derive CSWS-10 factors. EFA produced three factors that accounted for (60.386%) of the total variance of the scale. The results are shown in Table 4.

Table 4

Rotated Component Matrix^a

	Component
	1	2	3
W1			0.313
W2			0.813
W3	0.783
W4	0.757
W5		0.431
W6		0.839
W7		0.719
W8		0.471
W9			0.745
W10	0.813
Total of variance	22.236	19.269	18.858

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Confirmatory Factor Analysis CFA was used to validate the PWB factors. The results showed that there are appropriate goodness fit indexes (CMIN/DF = 1.671; NFI = 0.822; CFI = 0.920; RMSEA =0.054), all of which indicated that the PWB standards proposed model is acceptable. All of the Hope observed variables had statistically significant (p < 0.001) loadings on the respective latent variables (see Fig. 4).

Fig. 4

Confirmatory factor analysis of the hope standards proposed model.

3.4Data analysis

The data collected from the study participants were analyzed quantitatively. One-way ANOVA was calculated to reveal the study variables’ differences due to age, level of education, marital status, and income level variables (IBM SPSS statistics version 21). Exploratory Factor Analysis EFA using to extract the factors of each scales prepared in this study, and then using Confirmatory Factor Analysis CFA to validate this factor (IBM SPSS Amos 25). The Structural Equation Model was used to test the direct and indirect effects between study variables and validate the mediating role of religiosity and hope in the relationship between the stigma associated with COVID-19 and psychological well-being.

4.1The results about the differences in religiosity, hope, stigma, and psychological well-being due to age: one-way ANOVA calculated to detect the differences in religiosity, hope, stigma, and PWB. The findings are shown in Tables 5, 6, 7

The results shown in Table 6 indicated significant differences due to age in Hope and psychological well-being, while there are no differences in religiosity and stigma due to age. A Scheffe test was used to determine the direction of the differences (see results in Table 7). The results indicate the differences in hope and psychological well-being were in favor of members belonging to the age group from 30 years old and over.

4.2The results about the differences in religiosity, hope, stigma, and psychological well-being due to education level: one-way ANOVA calculated to detect the differences in religiosity, hope, stigma, and psychological well-being. The findings are shown in Tables 8, 9

The results shown in Table 9 indicated that there are no statistically significant differences due to education status in religiosity, hope, stigma, and psychological well-being among COVID-19 patients.

4.3The results about differences in religiosity, hope, stigma, and psychological well-being due to social status: one-way ANOVA calculated to detect the differences in religiosity, hope, stigma, and psychological well-being. The findings are shown in Tables 10, 11, 12

The results shown in Table 11 indicated statistically significant differences due to social status in psychological well-being while there are no differences in religiosity, hope, and stigma among COVID-19 patients. A Scheffe test was used to determine the direction of the differences (see results in Table 12). The results indicate that the differences in psychological well-being were in favor of the sample members belonging to the married group of COVID-19 patients.

4.4The results about the differences in religiosity, hope, stigma, and psychological well-being due to income level: one-way ANOVA calculated to detect religiosity, hope, stigma, and psychological well-being among COVID-19 patients. The findings are shown in Tables 13, 14

The results shown in Table 14 indicated no statistically significant differences due to income level in religiosity, hope, stigma, and psychological well-being among COVID-19 patients.

4.5The results about the intermediate role of religiosity and hope variables between stigma and psychological well-being among patients of COVID-19. The researchers drew a diagram model of the relationships between the study variables (see Fig. 5). To test this proposed model, structural equation modeling was used by the Maximum Likelihood Estimation method to find out the effects of religiosity and hope on stigma and psychological well-being as intermediate variables

To verify the fitness of the proposed model, the researchers used several indicators (Table 15) such as Chi-square divided in Degree of Freedom CMIN/DF (2.645), which is not insignificant value, and a Goodness Fit Index GFI (.96), the Root Mean Square Error of Approximation RAMSE (.062), which indicates that the proposed model is good. The results shown in Fig. 6 told that all paths of the model are statistically significant.

Fig. 5

The structural equation model for the relation between religiosity, hope, stigma, and PWB.

Fig. 6

The structural equation model for the relation between religiosity, hope, stigma, and psychological well-being.

The results shown in Table 16 indicated that the direct (unmediated) effect of hope on psychological well-being is 7.236, which means when hope goes up by 1, psychological well-being (PWS) goes up by 7.236. As well as, the direct (unmediated) effect of religiosity on psychological well-being is.807. When religiosity goes up by 1, psychological well-being goes up by 0.807. Also, the direct (unmediated) effect of stigma on PWB is 1.000. When stigma goes up by 1, psychological well-being goes up by 1.

The results shown in Table 17 indicated that the indirect (mediated) effect of hope on stigma is 0.000. Also, the indirect (mediated) effect of hope on psychological well-being is –4.147. As well as the in-direct (mediated) effect of religiosity on stigma is 0.000. Results about the indirect (mediated) effect of religiosity on psychological well-being are –.267. That is, due to the indirect (mediated) effect of religiosity on psychological well-being. As well as the indirect (mediated) effect of stigma on well-being is 0.000.

The results on regression in Table 18 indicated that the regression weight for religiosity in the prediction of stigma is not significantly at the 0.05 level (two-tailed). Simultaneously, the regression weight for hope in the prediction of stigma is significantly at the 0.001 level (two-tailed). Also, the regression weight for hope in predicting psychological well-being is significantly at the 0.001 level (two-tailed). The regression weight for religiosity in the prediction of psychological well-being is significantly at the 0.05 level (two-tailed). Findings also revealed that the regression weight for stigma in predicting psychological well-being is significantly at the 0.001 level (two-tailed).