Abstract
Purpose
This study is conducted in order to examine the impact of social exclusion on life satisfaction and psychological well-being among the elderly people.
Methods
Being descriptive cross-sectional, this study was conducted with 517 elderly adults in Turkey. Data were collected using a socio-demographic information form, the Social Exclusion Scale for the Elderly (SESE), the Life Satisfaction Scale for the Elderly (LSSE), and the Psychological Well-Being Scale for the Elderly (PWBSE).
Results
The mean age of the participants was 69.91 ± 5.63 years (Min-Max.=65–96). They scored an average of 60.87 ± 16.15 points from the SESE, a total of 46.64 ± 11.01 points from the LSSE and a total of 48.53 ± 11.06 points from the PWBSE. Social exclusion, life satisfaction, and psychological well-being levels were found to be above average. Higher education and income levels were associated with lower social exclusion and higher life satisfaction and psychological well-being (p < .05). A significant negative relationship was found between SESE and both LSSE and PWBSE scores (r=–.087 to –0.292, p < .05). Regression analyses showed that social exclusion explained 1.1% of life satisfaction and 2.3% of psychological well-being. When qualitative and quantitative variables were added to both models, the percentage of explanation increased to 47.7% and 51.5%, respectively.
Conclusion
Social exclusion is thought to affect life satisfaction and psychological well-being in older individuals, but it is estimated to have a greater impact on psychological well-being. Interventions aimed at improving social inclusion—particularly those addressing education, income, and living environment—may enhance the well-being of elderly. Community health nurses can play a key role in implementing supportive and inclusive strategies.
Keywords: Elderly, Social exclusion, Life satisfaction, Psychological well-being
Introduction
Social exclusion is a complex and dynamic process, and it contributes to the inability of individuals in society to protect their social, economic, political or cultural rights or to enable their participation in these areas [1]. Elderly, due to their physical and mental limitations, experience social exclusion more prominently compared to other age groups [2, 3].
The concept of social exclusion, which refers to the deprivation or denial civil, political, economic and social citizenship rights of the individual, has become a focal point since the second half of the 1990s [4, 5]. The acceleration of initiatives to develop “age-based social policies” with the increase in the global elderly population has brought attention to studies on social exclusion in the elderly [6–8]. Current aging strategies address various issues related to social exclusion including elderly persons’ participation in society at a level appropriate to their capacities, providing them with the protection, security and care they need, in relation to the ‘active aging’ process. However, the current lack of understanding regarding the factors and mechanisms contributing to social exclusion remains a major obstacle to addressing this problem [9, 10].
The concept of life satisfaction, which appears as the fulfillment of life-related expectations of the individual in elderly adults, is positively influenced by factors such as cognitive competence, income level, education level, health status, and sleep quality, as well as social functionality and social support [11]. The literature indicates that elderly adults who experience loneliness due to social exclusion or social isolation face negative impacts on their life satisfaction and there is a significant decline in their quality of life [12, 13].
Another concept related to social exclusion is psychological well-being. Psychological well-being, defined as the state of an individual’s life functioning smoothly, without significant dysfunction, and characterized by feeling good, expresses inner peace, emotional balance, and psychological comfort in elderly individuals [14]. Elderly adults who have opportunities to communicate with others, actively participate in social life, live with family members or relatives, and do not feel lonely have higher psychological well-being [15]. It has been reported in numerous studies that social exclusion or enforced social isolation (e.g., during COVID-19 lockdowns) has detrimental effects on both the physical and mental health of elderly adults, and that negative impact on the psychological well-being of elderly people is higher than expected [16–18]. Conceptual models of social exclusion among elderly emphasise that psychological resources and socio-emotional processes can influence how social relationships are evaluated and, consequently, how social exclusion is experienced. In old age, there is a higher likelihood of experiencing devastating losses, such as fatal diagnoses or the death of a spouse, and an inability to manage these losses effectively can lead to social exclusion and negative consequences [1]. Conversely, positive social relationships contribute to elderly individuals’ well-being and quality of life. Positive social relationships are an important source of satisfaction for elderly [19].
One of the primary challenges faced by aging societies is how to prevent the social exclusion of elderly adults [20]. It has been found that the elderly population grew by an average of 70.5% worldwide, 98.5% in the EU, and 149.6% in Turkey in 2016 compared to 1960. Today, in Türkiye, which has an elderly population rate of 10.2% slightly above the global average of approximately 10.0%. It is expected that by the year 2100, the proportion of the elderly population will reach approximately 22.5% worldwide, 30.4% in Europe and 32.6% in Turkey [21]. Due to the growing elderly population and the vulnerability of the elderly to social exclusion, there is an urgent need to plan interventions aimed at preventing social exclusion of the elderly.
In light of the growing literature on social exclusion in old age, systematic reviews have primarily focused on participation in civic activities, exclusion from essential services, and social relationships [22, 23]. A limited number of studies have examined psychosocial factors such as life satisfaction [8, 24], and psychological well-being [25, 26].
Although social policies and health systems for the elderly vary from country to country, they play a significant role in the overall well-being of older individuals in each nation [24, 27]. In this context, this study, which explores the relationship between the level of social exclusion of elderly individuals in Turkish society and levels of life satisfaction and psychological well-being, answers to the following questions are sought:
What are the levels of social exclusion, life satisfaction, and psychological well-being among elderly individuals?
Is there a significant relationship between the socio-demographic characteristics of elderly individuals and their levels of social exclusion, life satisfaction, and psychological well-being?
Is there a significant relationship between social exclusion, life satisfaction, and psychological well-being among elderly individuals?
To what extent does social exclusion predict life satisfaction and psychological well-being among elderly individuals?
Materials and methods
Data collection
This descriptive cross-sectional and correlational study was conducted online between March 15 and July 08, 2024 using a web-based questionnaire (online survey via Google form). For this purpose, the online survey link was shared with elderly individuals or illiterate participants (data collection through legal representatives and telephone interviews) through social networking sites in Turkey (any social media platform: Watsapp, Facebook, Messenger) and the researchers’ personal social media accounts. To reach the target group, information was shared through online groups and social networks involving elderly individuals. To prevent duplicate responses, Google Forms settings that limit repeated participation from the same device were used. Eligibility questions were included at the beginning of the survey to assess whether participants met the inclusion criteria, and only individuals meeting the criteria were included in the survey.
Population and sample
The population of the study comprised of 8,451,669 elderly individuals living in Turkey [28]. Since the data were collected online, convenience sampling, which enables quick, easy and cost-effective data collection from the population, was used in the study [29]. The study was completed with 517 elderly who met the inclusion criteria and voluntarily consented to participate by marking the agreement on the home page of the survey.
Inclusion criteria
Being 65 years of age or older, having access to the online survey link, being able to use electronic devices or answer the questions asked by the researcher, and volunteering to participate in the study.
Exclusion criteria
Being 64 years of age or younger, not being willing to participate in the study, and being unable to answer the questions asked by the researcher.
Data collection tools
Data were collected using the Socio-Demographic Information Form, the Social Exclusion Scale for the Elderly (SESE), Life Satisfaction Scale for the Elderly (LSSE), and the Psychological Well-Being Scale for the Elderly (PWBSE).
Socio-demographic information form
It is comprised of variables about age, gender, marital status, place of residence, education level, income level, presence of chronic diseases, smoking and alcohol habits [6, 30–33].
SESE
Developed and validated for Turkish society by Apak and Apak, the SESE consists of 22 items rated on a 5-point Likert scale. While 21 items are positive, one item (Item 14) is negative and reverse-scored. The scale comprises four subscales: Financial Deprivation, Informal Social Support, Transportation and Financial Services, and Environmental Exclusion. The total score from these subscales constitutes the ‘overall social exclusion score for the elderly’, where higher scores indicate greater levels of social exclusion. The scale’s overall Cronbach’s alpha reliability coefficient is 0.86 [6]. In this study, the Cronbach’s alpha value of the scale was found to be 0.917. Confirmatory factor analysis (CFA) results indicated that all three scales demonstrated acceptable model fit. For the SESE scale, the fit indices were CMIN/DF = 3.893, RMR = 0.099, GFI = 0.877, AGFI = 0.829, NFI = 0.885, CFI = 0.912, IFI = 0.912, TLI = 0.889, and RMSEA = 0.075, suggesting an overall acceptable level of fit.
LSSE
The scale developed by Altay and Çalmaz in 2022 consists of 14 items and 3 subscales. The sub-dimensions of the scale are Self-acceptance (Factor 1), Motivation (Factor 2) and Peace of Mind (Factor 3). In the 5-point Likert-type scale, items are scored between 1 and 5 as “strongly disagree . strongly agree”. The minimum total that can be obtained from the scale is 14 and the maximum is 70. Cronbach Alpha coefficient is found to be 0.874 [32]. In this study, the Cronbach’s alpha value of the scale was found to be 0.934. For the SELE scale, the model fit indices were CMIN/DF = 4.096, RMR = 0.048, GFI = 0.925, AGFI = 0.882, NFI = 0.940, CFI = 0.954, IFI = 0.954, TLI = 0.937, and RMSEA = 0.077. These values indicate a good level of model fit.
PWBSE
Developed by Gümüş Demir in 2022, the PWBSE includes 15 items rated on a 5-point Likert scale. The scale does not have a defined cutoff point; higher scores indicate higher levels of psychological well-being. The total possible score ranges from 15 to 75, with Items 8 and 13 reverse-scored. The Cronbach’s alpha reliability coefficient for the scale is found out to be 0.89 [33]. In this study, the Cronbach’s alpha value of the scale was found to be 0.913. Similarly, the PWBSE scale showed fit indices of CMIN/DF = 4.154, RMR = 0.036, GFI = 0.938, AGFI = 0.892, NFI = 0.961, CFI = 0.970, IFI = 0.970, TLI = 0.956, and RMSEA = 0.078, reflecting a good and acceptable model fit.
Data evaluation
The statistical analysis of the data obtained in the study was conducted using the Statistical Package for Social Sciences (SPSS) version 27.0. The normality of the data was assessed using the Skewness-Kurtosis values within the ± 2 range [34]. Descriptive statistical methods (mean, standard deviation, frequency, and percentage) were used to summarize the data. For data comparisons, independent sample t-tests, One-Way ANOVA, Pearson correlation analysis, simple and multiple linear regression analysis were performed. The assumption of homogeneity of variance was evaluated using the Levene test, and regression linearity was evaluated using histogram graphs. The statistical significance level was set at p < .05 [35]. Scale means were used in comparing statistical findings (Tables 3, 4, 5 and 6).
Table 3.
Comparison of SESE, LSSE, and PWBSE total scale scores based on participants’ socio-demographic characteristics (N = 517)
| Socio-Demographic Variables | n (%) | SESE | LSSE | PWBSE | |
|---|---|---|---|---|---|
| X̄± SD | X̄± SD | X̄± SD | |||
| Place of Residence | Province1 | 218 (42.2) | 2.67±0.79 | 3.33±0.84 | 3.24±0.78 |
| District2 | 199 (38.5) | 2.73±0.63 | 3.36±0.72 | 3.26±0.71 | |
| Village3 | 100 (19.3) | 3.04±0.72 | 3.25±0.77 | 3.15±0.67 | |
| Test value | F = 9.446 | F=0.741 | F=0.762 | ||
| Level of Significance |
p<.001* 3>1,2** |
p=.478 | p=.467 | ||
| Education level | Illiterate1 | 134 (25.9) | 2.79±0.68 | 3.17±0.74 | 3.13±0.66 |
| Literate2 | 75 (14.5) | 2.73±0.87 | 3.14±0.87 | 3.03±0.78 | |
| Primary school3 | 105 (20.3) | 2.89±0.60 | 3.31±0.75 | 3.15±0.70 | |
| Secondary school4 | 61 (11.8) | 2.73±0.73 | 3.45±0.78 | 3.38±0.75 | |
| High school5 | 82 (15.9) | 2.80±0.73 | 3.42±0.78 | 3.30±0.81 | |
| University6 | 60 (11.6) | 2.50±0.81 | 3.67±0.70 | 3.59±0.61 | |
| Test value | F = 2.375 | F = 4.906 | F = 5.702 | ||
| Level of Significance |
p=.038* 6<3,5** |
p<.001* 6>1.2,3** |
p<.001* 6>1,2,3,5** |
||
| Income level | Low | 60 (11.6) | 3.55±0.71 | 2.81±0.91 | 2.61±0.64 |
| Medium | 348 (67.3) | 2.81±0.60 | 3.31±0.71 | 3.20±0.66 | |
| High | 109 (21.1) | 2.17±0.63 | 3.67±0.77 | 3.66±0.74 | |
| Test value | F = 98.639 | F = 25.713 | F = 46.858 | ||
| Level of Significance |
p<.001* 3<1,2 |
p<.001* 3>1,2** |
p<.001* 3>1,2** |
||
| Alcohol consumption | Yes | 38 (7.4) | 2.52±0.82 | 3.39±0.83 | 3.46±0.94 |
| No | 479 (92.6) | 2.78±0.72 | 3.32±0.78 | 3.21±0.71 | |
| Test value | t=-2.155 | t=0.499 | t = 1.997 | ||
| Level of Significance | p=.032 | p=.618 | p=.046 | ||
Numbers marked with a superscript were used to identify the group that made the difference
F ANOVA Variance Analysis test value, t t Test value in independent groups,
*p<.001, **Tukey Test
Table 4.
The relationship between the scale scores of the participants in the SESE, LSSE and PWBSE (N = 517)
| Variables | 1 | 1(A) | 1(B) | 1(C) | 1(D) | 2 | 2(A) | 2(B) | 2(C) | 3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1-SESE Totals | r | 1 | 0.890** | 0.711** | 0.782** | 0.776** | − 0.104* | − 0.084 | − 0.104* | − 0.116** | − 0.292** |
| p | 0.000 | 0.000 | 0.000 | 0.000 | 0.018 | 0.055 | 0.018 | 0.008 | 0.000 | ||
| 1(A)-Financial Deficiency | r | 1 | 0.405** | 0.652** | 0.543** | − 0.128** | − 0.103* | − 0.068 | − 0.187** | − 0.299** | |
| p | 0.000 | 0.000 | 0.000 | 0.003 | 0.019 | 0.120 | 0.000 | 0.000 | |||
| 1(B)-Informal social support | r | 1 | 0.385** | 0.640** | − 0.022 | − 0.025 | − 0.121** | 0.067 | − 0.123** | ||
| p | 0.000 | 0.000 | 0.617 | 0.571 | 0.006 | 0.129 | 0.005 | ||||
| 1(C)-Transportation and social services | r | 1 | 0.480** | − 0.039 | − 0.017 | − 0.001 | − 0.114** | − 0.254** | |||
| p | 0.000 | 0.374 | 0.701 | 0.978 | 0.009 | 0.000 | |||||
| 1(D)- Environmental Exclusion | r | 1 | − 0.116** | − 0.104* | − 0.174** | − 0.062 | − 0.215** | ||||
| p | 0.008 | 0.019 | 0.000 | 0.160 | 0.000 | ||||||
| 2- LSSE Totals | r | 1 | 0.970** | 0.822** | 0.772** | 0.678** | |||||
| p | 0.000 | 0.000 | 0.000 | 0.000 | |||||||
| 2(A)-Self acceptance | r | 1 | 0.734** | 0.622** | 0.683** | ||||||
| p | 0.000 | 0.000 | 0.000 | ||||||||
| 2(B)-Motivation | r | 1 | 0.590** | 0.525** | |||||||
| p | 0.000 | 0.000 | |||||||||
| 2(C)-Peace of mind | r | 1 | 0.475** | ||||||||
| p | 0.000 | ||||||||||
| 3-PWBSE Totals | r | 0.475** | |||||||||
| p | 0.000 | ||||||||||
Numbers in bold indicate statistical significance
*Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed)
Table 5.
Relationship of SESE, PWBE, and sociodemographic variables with LSSE
| B | SE | Beta | t | p | 95.0% Confidence Interval for B |
|||
|---|---|---|---|---|---|---|---|---|
| Lower Bound |
Upper Bound |
|||||||
| Dependent variable: LSSE Total | (Constant) | 3.626 | 0.134 | 27.033 | < 0.001 | 3.362 | 3.889 | |
| SESE Total | − 0.014 | 0.006 | − 0.294 | -2.423 | 0.016 | − 0.026 | − 0.003 | |
| F(1, 515) = 5.649; R=.104; R2=0.011; Adjusted R2=0.009; Durbin Watson = 1.954; p=.004 | ||||||||
| Dependent variable: LSSE Total | (Constant) | -1.152 | 5.452 | − 0.211 | 0.833 | -11.863 | 9.559 | |
| SESE Total | 0.101 | 0.026 | 0.148 | 3.907 | < 0.001 | 0.050 | 0.152 | |
| PWBSE Total | 0.670 | 0.035 | 0.673 | 19.235 | < 0.001 | 0.601 | 0.738 | |
| Age | 0.057 | 0.063 | 0.029 | 0.902 | 0.367 | − 0.067 | 0.180 | |
| Education level | 0.342 | 0.218 | 0.053 | 1.567 | 0.118 | − 0.087 | 0.770 | |
| Income level | 1.955 | 0.798 | 0.100 | 2.452 | 0.015 | 0.389 | 3.522 | |
| F(5, 516) = 95.016; R=.694; R2=0.484; Adjusted R2=0.477; Durbin Watson = 2.036; p<.001 | ||||||||
Numbers in bold indicate statistical significance
Table 6.
Relationship of LSSE, SESE and sociodemographic variables with PWBE
| B | SE | Beta | t | p | 95.0% Confidence Interval for B | |||
|---|---|---|---|---|---|---|---|---|
| Lower Bound |
Upper Bound |
|||||||
|
Dependent variable: PWBSE Total |
(Constant) | 60.718 | 1.820 | 33.369 | 0.000 | 57.143 | 64.293 | |
| SESE Total | − 0.200 | 0.029 | − 0.292 | -6.928 | 0.000 | − 0.257 | − 0.143 | |
| F(1. 515) = 48.001; R=.172; R2=0.029; Adjusted R2=0.083; Durbin Watson = 1.897; p< 001 | ||||||||
|
Dependent variable: PWBSE Total |
(Constant) | 22.738 | 5.179 | 4.391 | <0.001 | 12.564 | 32.913 | |
| SESE Total | − 0.116 | 0.025 | − 0.169 | -4.642 | <0.001 | − 0.165 | − 0.067 | |
| LSSE Total | 0.627 | 0.033 | 0.624 | 19.235 | <0.001 | 0.563 | 0.691 | |
| Age | − 0.018 | 0.061 | − 0.009 | − 0.299 | 0.765 | − 0.138 | 0.101 | |
| Education level | 0.145 | 0.211 | 0.023 | 0.687 | 0.493 | − 0.270 | 0.560 | |
| Income level | 2.104 | 0.771 | 0.107 | 2.730 | 0.007 | 0.590 | 3.618 | |
| F(5. 516) = 110.368; R=.721; R2=0.519; Adjusted R2=0.515; Durbin Watson = 2.060; p<.001 | ||||||||
Numbers in bold indicate statistical significance
Ethical considerations
Ethical approval for the study was obtained from the Non-Interventional Research Ethics Committee of the Faculty of Health Sciences of Kafkas University (dated 29.02.2024, protocol number 81829502.903/32). Permission to use the scales included in the study was obtained via email. This study was conducted in accordance with the Declarations of Helsinki and the STROBE guidelines for observational studies. Additionally, participants declared online that they had read the informed consent text and agreed to participate in the study before starting to answer the survey. Voluntary informed consent was obtained from illiterate participants through telephone interviews with their legal representatives by the researchers, and the responses given by the older individual were marked on a Google Form. It was explained that the participants’ information was confidential and that they had the right to withdraw from the study.
Results
The mean age of the participants was 69.91 ± 5.63 years (Mn.-Max.=65–96 age). Among the participants, 52.4% were male, 83.4% were married, 42.2% resided in urban areas, and 25.9% were illiterate. Additionally, 67.3% reported a moderate income level, 76% had a chronic illness, 20.1% were diagnosed with hypertension, 65.8% did not smoke, and 92.6% did not consume alcohol (Table 1).
Table 1.
Distribution of socio-demographic characteristics of participants (N = 517)
| n | % | ||
|---|---|---|---|
| Gender | Female | 246 | 47.6 |
| Male | 271 | 52.4 | |
| Marital Status | Married | 431 | 83.4 |
| Single | 86 | 16.6 | |
| Place of residence | Province | 218 | 42.2 |
| District | 199 | 38.5 | |
| Village | 100 | 19.3 | |
| Education level | Illiterate | 134 | 25.9 |
| Literate | 75 | 14.5 | |
| Primary school | 105 | 20.3 | |
| Secondary school | 61 | 11.8 | |
| High school | 82 | 15.9 | |
| University | 60 | 11.6 | |
| Level of Income | Low | 60 | 11.6 |
| Medium | 348 | 67.3 | |
| High | 109 | 21.1 | |
| Chronic disease | Yes | 393 | 76.0 |
| No | 124 | 24.0 | |
| Type chronic disease | No chronic disease | 124 | 24.0 |
| Other | 30 | 5.8 | |
| Diabetes | 103 | 19.9 | |
| Hypertension | 104 | 20.1 | |
| Heart disease | 60 | 11.6 | |
| Osteoporosis | 8 | 1.5 | |
| Prostate disease | 17 | 3.3 | |
| Depression | 12 | 2.3 | |
| COPD | 52 | 10.1 | |
| Stroke | 7 | 1.4 | |
| Smoking | Yes | 177 | 34.2 |
| No | 340 | 65.8 | |
| Alcohol consumption | Yes | 38 | 7.4 |
| No | 479 | 92.6 | |
| X̄ ± SD (Min–Max) | |||
| Age 69.91 ± 5.63 (65–96) | |||
Elderly individuals scored an average of 60.87 ± 16.15 on SESE, 46.64 ± 11.01 on LSSE, and 48.53 ± 11.06 on PWBSE (Table 2).
Table 2.
Distribution of total and subscale scores for SESE, LSSE, and PWBSE (N = 517)
| Variables | X̄ ± SD | Min-Max | Item No |
|---|---|---|---|
| SESE Totals | 60.87 ± 16.15 | 22.00-106.00 | 22 items |
| Financial deprivation | 26.21 ± 8.14 | 9.00–44.00 | 9 items |
| Informal social support | 12.57 ± 4.54 | 5.00–25.00 | 5 items |
| Transportation and financial services | 12.13 ± 4.00 | 4.00–20.00 | 4 items |
| Environmental exclusion | 9.93 ± 3.26 | 4.00–20.00 | 4 items |
| LSSE Totals | 46.64 ± 11.01 | 14.00–70.00 | 14 items |
| Self acceptance | 30.79 ± 7.68 | 9.00–45.00 | 9 items |
| Motivation | 7.02 ± 1.86 | 2.00–10.00 | 2 items |
| Peace of mind | 8.82 ± 2.62 | 3.00–15.00 | 3 items |
| PWBSE Totals | 48.53 ± 11.06 | 18.00–75.00 | 15 items |
Upon examining Table 3; SESE scores were found to be statistically significantly lower for those who lived in provinces and districts than those living in villages, for those with university degrees than those with primary and high school degrees, for those with good income than those with low and medium income, and for those who do not consume alcohol than those who consume alcohol (p<.05). In terms of LSSE scores, it was found that participants with a university degree scored significantly higher compared to those who were illiterate, literate or primary and high school graduates. Likewise, individuals with a higher income level scored significantly higher than those with low or moderate income levels, and those who consumed alcohol scored significantly higher than those who did not (p<.05). There were no statistically significant results between the participants’ gender, marital status, chronic disease status, type of chronic disease, smoking status, and the Scale Scores of the SESE, LSSE and PWBSE (p>.05).
The study identified a significant negative and low-level relationship between participants’ total SESE scores and their total LSSE scores, LSSE subscale scores, and total PWBSE scores (r = Min-0.087 to Max-0.292, p<.005). It was observed that as the total LSSE and PWBSE scores and their subscale scores increased, the total SESE scores decreased significantly. This indicates that as elderly individuals’ life satisfaction and psychological well-being levels increase, their social exclusion levels decrease significantly (Table 4).
Regression model was used to determine the effects of social exclusion and its sub-dimensions on life satisfaction, and statistical estimates of the model affirmed that the model was significant and usable (Table 5; p<.001). It was observed that social exclusion scale explained 1.1% of life satisfaction [F(1, 515) = 5.649, R2=0.011]. When multiple linear regression was performed by adding PWBSE, age, education status and economic status to the model in addition to SESE, it was found that social exclusion explained 47.7% of life satisfaction (F(5, 516) = 95.016, Adjusted R2=0.477).
Regression model was applied to determine the effects of the participants’ social exclusion scale on their psychological well-being levels, and statistical estimates of the model showed that the model was significant and usable (Table 6; p<.001). Social exclusion alone was found to explain 2.9% of psychological well-being [F(1, 515) = 48.000, R2=0.029]. When multiple linear regression was performed by adding LSSE, age, education level, and economic status to the model in addition to SESE, it was found that social exclusion explained 51.5% of life satisfaction (F(5, 516) = 110.368, Adjusted R2=0.515).
Discussion
In the present study, findings regarding the association between social exclusion and life satisfaction and psychological well-being among elderly individuals were discussed in the context of the literature, considering the increasing relevance of social exclusion in parallel with global population aging.
The study determined that the social exclusion levels among elderly individuals were above average, showing a positive and significant trend. Financial deprivation was found to be the factor most strongly associated with social exclusion among the elderly, whereas environmental exclusion showed the weakest association. Similar findings have been reported in the literature [36, 37]. Apak et al. found that older adults individuals living in rural areas experienced the highest exclusion in transportation and financial services, followed by environmental exclusion and material deprivation [36]. The strong association between financial deprivation and social exclusion among older adults may be explained by the role of economic resources in shaping quality of life and social participation, as well as by the increasing care needs in later life; this, in turn, may limit participation in social activities and contribute to deeper social isolation. Furthermore, this finding indicates that social support alone is insufficient and points to the multidimensional nature of social exclusion.
In this study, the life satisfaction levels of elderly individuals were found to be above average, positive and significant. Similar findings supporting this study, studies reporting high life satisfaction scores among elderly individuals, are also available in the literature [38, 39]. In contrast to these findings, Kaçan et al. found that older adults individuals residing in nursing homes reported low levels of life satisfaction [40]. These results indicate that life satisfaction among older adults is related to multiple factors. Therefore, identifying and addressing the factors that determine life satisfaction among elderly is considered important.
The psychological well-being levels of older adults individuals in this study were also determined to be above average, showing a positive and significant trend. In a study by Kurtkapan (2019), retirees living in urban areas were found to have moderate levels of psychological well-being, whereas Atay and Kumcaz reported higher levels of well-being among middle-aged individuals [41, 42]. This study identified that individuals aged 55–64, categorized as pre-older adults, had significantly higher psychological well-being compared to those aged 75 and above. These findings suggest that psychological well-being levels may vary with age and that individuals are exposed to different influences throughout their lifetimes. The higher well-being levels observed in younger elderly groups might stem from factors such as better physical health, greater social participation, or more favorable living conditions during this period.
In this study, university-level education was found to be associated with lower levels of social exclusion and higher levels of life satisfaction and psychological well-being. In the literature, there are studies reporting that social exclusion levels decrease as age, income status and education level increase [36, 37, 43–46]. In the study by Inak et al., it was determined that variables such as gender, education, age, occupation, and income can either reduce or increase social exclusion among working poor individuals [43]. Similarly, Kurtkapan et al. reported that higher income levels were associated with better living standards among elderly individuals and higher levels of psychological well-being [41]. Based on the findings and existing literature, it can be deduced that factors that enhance the living standards of the older adults and facilitate their inclusion in society can decrease social exclusion while increasing life satisfaction and psychological well-being. Education, in this context, emerges as a critical tool for contributing to both individual and societal well-being.
This study found that non-drinkers had significantly lower levels of social exclusion compared to drinkers; however, drinkers had significantly higher levels of psychological well-being compared to non-drinkers. Previous studies have shown that social influences may be associated with individuals’ health behaviors [37] and that factors such as retirement, chronic diseases, and social isolation may increase the risk of alcohol consumption among older adults [47, 48]. There are also studies that identify alcohol as a symbolic and functional factor, particularly among the older adults, facilitating socialization, supporting emotional regulation, and contributing to the maintenance of a positive self-image [49]. When these findings are considered together, the results suggest that differences in social exclusion and psychological well-being among alcohol-consuming groups may reflect a complex model involving social and emotional functions.
This study found that place of residence is associated with social exclusion; individuals living in cities and districts were found to have lower social exclusion scores compared to those living in villages.A study conducted in Finland similarly found that the risk of loneliness was found to be higher in elderly people living alone or in rural areas (compared to an urban centers) [50]. It has been argued that limited access to resources and activities in living spaces due to social exclusion is considered a contributing factor to loneliness, and conceptually, exclusion from social relationships increases the risk of loneliness [1, 7]. The lower social exclusion scores of elderly people living in cities and towns compared to those living in villages can be attributed to the social opportunities, access to health and care services, access to public transportation and other facilities available in urban areas. On the other hand, strong communal bonds in villages don’t mean to provide social support, the fast-paced lifestyle and individualization in the city do not completely eliminate the risk of loneliness and exclusion.
This study found that as life satisfaction and psychological well-being levels increased among elderly individuals, social exclusion significantly decreased. Previous studies have reported an association between social factors and individuals’ health behaviors [3, 8]. For instance, Kim et al. found a longitudinal association between cumulative social isolation and reduced life satisfaction in elderly, particularly highlighting persistent risks for women [51]. Similarly, Tain and Chen emphasized that life satisfaction and subjective well-being are the primary keys to healthy aging [38]. A study conducted on elderly individuals living in Shanghai indicated that income insufficiency, poor housing conditions, and feelings of loneliness were significant predictors of social exclusion, which was also linked to increased depressive symptoms among socially excluded elderly individuals [52]. Tomida et al. demonstrated that experiences of social isolation were associated with higher levels of loneliness and lower life satisfaction [53]. In a study comparing Japan and the UK, it was expressed that there were more socially isolated elderly individuals in Japan, which was associated with depression and mortality [54]. Meta-analyses have further provided evidence that weak social connections can adversely affect physiological health, potentially leading to premature mortality. High levels of loneliness, infrequent social contact, and low group participation have been linked to an increased risk of dementia and other outcomes such as poor psychological health, diminished quality of life, and well-being [55–57]. Social exclusion in old age is a process resulting from the interaction of multi-level structural factors such as economic deprivation, loss of autonomy, spatial inequalities, and limited social networks, negatively impacting both physical and mental health. This is associated with decreased life satisfaction and psychological well-being, and an increase in depression and other mental health problems, and is considered a significant social determinant of the overall well-being of older adults.
This study determined that social exclusion has a higher explanatory power on psychological well-being compared to life satisfaction. However, the limited explanatory power of both models, and especially the relatively low adjusted R² value for life satisfaction, indicate that the effect of social exclusion on these variables is limited. In one study, it was determined that social support and social isolation played a mediating role in the life satisfaction of adults [58]. In another study, social isolation was found to be associated with psychological distress and lower levels of life satisfaction and happiness [59]. Teke et al. reported that the primary issue faced by the older adults is health problems (44%), followed by loneliness and social exclusion (41%) [46].Another study revealed a negative association between life satisfaction and social exclusion, indicating that social exclusion, though limited, plays a role in explaining variations in life satisfaction [60, 61]. Social exclusion is a multidimensional process encompassing reduced life satisfaction and psychological well-being, decreased participation in social networks, weakened sense of belonging, and lack of social support. This condition is considered a complex risk pattern characterized by weakened connections between individuals and social, economic, and psychological systems, thereby requiring multidimensional intervention approaches. These interventions should encompass not only individual-level factors but also broader social and structural dimensions, including community-based programs that promote social participation, age-friendly policies, and initiatives that strengthen social support networks for older adults.
Conclusion and recommendations
This study examined the relationships between social exclusion, life satisfaction, and psychological well-being in older individuals. The study found that older adults exhibited above-average levels of social exclusion, life satisfaction, and psychological well-being. The findings indicate a significant negative correlation between the levels of social exclusion among older individuals and their life satisfaction and psychological well-being. Accordingly, it was determined that as life satisfaction and psychological well-being increase, the level of social exclusion decreases. When examined in terms of socio-demographic variables, life satisfaction increases and social exclusion decreases as education level and income status rise. Furthermore, it was found that social exclusion alone has a low explanatory power on life satisfaction and psychological well-being; however, when variables such as psychological well-being, age, education, and economic status are added to the model, the explanatory power significantly increases. This indicates that the well-being of older individuals is affected by multidimensional factors. Overall, it was concluded that social exclusion is a significant determinant of life satisfaction and psychological well-being among older individuals, and that there is a significant interaction between these variables.
Based on these results: in public health and gerontology nursing practices, priority should be given to interventions aimed at reducing social exclusion and increasing life satisfaction and psychological well-being among older adults. Social support systems and community-based programs that will increase the social participation of individuals, especially those with low levels of education and income, should be strengthened.Policies should focus on reducing socioeconomic inequalities that contribute to social exclusion in older populations.Healthcare professionals, especially nurses working in community and senior care settings, should assess social exclusion as part of routine evaluations.More representative sampling methods and longitudinal designs should be used in future studies to better understand causal relationships.
Strengths and limitations of the study
The main weakness of the study is the low representativeness of the sample, as participation was limited to elderly who had access to the online survey or, for those who were illiterate, could be interviewed by telephone and complete the study. In this case, the findings obtained in the research cannot be generalized to the entire elderly population. The second limitation is that, although the participants/relatives’ demonstrated the technological competence necessary to access the online survey, their cognitive abilities to fully comprehend and accurately respond to the questions, as well as any underlying psychiatric diagnoses, remain unknown. The final limitation is that this study did not investigate factors such as living alone or physical functional impairments, which could lead to involuntary social isolation.
Another limitation is that the digital divide can create sampling bias, which is a significant methodological issue in studies with the elderly population. The main reason for choosing a completely online convenience sampling method in this study is the accessibility, time, and cost constraints in the data collection process. Furthermore, face-to-face data collection opportunities were limited during the period the study was conducted. We believe that future studies using offline data collection methods (e.g., face-to-face or telephone interviews) in combination with online methods will provide more inclusive samples.
This study has several strengths: First, the relatively large sample size (N = 517) increases the statistical power of the study and supports the reliability of the findings. Second, the use of validated and reliable measurement tools (SESE, LSSE, and PWBSE) enhances the methodological rigor and credibility of the results. Third, the study simultaneously examines multiple psychosocial constructs, including social exclusion, life satisfaction, and psychological well-being, providing a comprehensive understanding of the well-being of older adults. Fourth, the inclusion of various sociodemographic variables (such as age, education, and income) allows for a more detailed analysis of factors associated with these outcomes.Finally, the use of multiple statistical methods, including correlation and regression analyses, strengthens the analytical depth of the study and contributes to a more robust interpretation of the relationships between variables.
Acknowledgements
The authors thank the participants who participated in this study.
Authors’ contributions
Gönül Gökçay: Conceptualization, Formal analysis, Methodology, Software, Data curation, Writing- Original draft preparation, Writing – review & editing. Ayşegül Parlak : Visualization, Investigation, Supervision, Writing- Reviewing and Editing Şenay Şener: Software, Validation, Writing- Reviewing and Editing.
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
Ethical approval for the study was obtained from the Non-Interventional Research Ethics Committee of the Faculty of Health Sciences of Kafkas university (dated 29.02.2024, protocol number 81829502.903/32). Permission to use the scales included in the study was obtained via email. This study was conducted in accordance with the Declarations of Helsinki and the STROBE guidelines for observational studies. Additionally, participants declared online that they had read the informed consent text and agreed to participate in the study before starting to answer the survey. Voluntary informed consent was obtained from illiterate participants through telephone interviews with their legal representatives by the researchers, and the responses given by the older individual were marked on a Google Form. It was explained that the participants’ information was confidential and that they had the right to withdraw from the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
No datasets were generated or analysed during the current study.