The sample consisted of 41 women and 44 men, aged between 60 and 86 years, and presented an equivalent number of self-declared white and black/mixed-race individuals, as shown in Table 1, which presents the general characteristics of the sample. The cut-off score for assessing the degree of vulnerability of the participants considered scores lower than 0.67 as high social vulnerability, and scores from 0.67 to 1 as low social vulnerability (Carvalho et al., 2021). Based on this criterion, 41 participants had a high degree of social vulnerability, and 44 presented a low degree of social vulnerability. In the sample with high social vulnerability, the lowest score found was 0.43, while for the sample with lower vulnerability, the highest score found was 0.96.

Table 1 Table 1 General Characteristics of the Sample Regarding the Variables Studied. Notes.

m.w = minimum wages; FDI = Family Development Index. ^a 1 m.w. = R$ 1412.00 = US$ 287.00

The general characteristics of the sample of individuals with high and low vulnerability are presented in Table 2. It is possible to observe a significant variation in income and education between the two groups. Analyzing the scores of the instruments used and considering the vulnerability variable, Table 2 also presents the general characteristics and the mean scores of the constructs studied.

Table 2 Table 2 General Characteristics of the Sample Regarding the Degree of Vulnerability. Notes.

m.w = Minimum wages; FDI = Family Development Index; CD-RISC = Connor-Davidson Resilience Scale; SRD = Satisfaction referenced to domains; MS = Mood state. ^a 1 m.w = R$ 1412.00 = US$ 287.00 ^b Only the scores of participants who reported violence were considered.

Considering vulnerability as a categorical variable, significant differences were found for age (t(71.4) = -3.93; p < 0.001; d = -0.85), education (t(56.7) = 7.75; p < 0.001; d = 1.66), income (t(44.6) = 4.01; p < 0.001; d = 0.85), and the number of people in the household (t(83) = 3.13; p = .002; d = 0.68) between the groups. In this sense, the results indicated that the sample with higher social vulnerability was, on average, older, had lower education, lower monthly income, and lived with fewer people compared to the group with lower vulnerability.

The sample of more vulnerable individuals showed lower mean scores in resilience, general life satisfaction, and mood compared to the low-vulnerability sample. However, the statistical analysis did not show a significant difference between the groups for any score.

The number of people who reported violence represents 25.9 % of the sample, with an equal number of people reporting violence in both groups. However, the score obtained was slightly higher in the greater vulnerability group. Most people reported only one type of violence, the most prevalent being psychological violence (e.g., being yelled at without reason, being called names or nicknames, or being threatened). Economic abuse was the second most frequently reported type, followed by physical violence (n = 2).

Pearson’s correlation analysis identified that the only significant correlations involving violence were with the domains Mood state (r = –0.22) and Satisfaction referenced to domains (r= –0.23). These correlations are presented in Table 3. Due to the limited number of significant correlations found between violence and the other variables using Pearson’s correlation, exploratory analyses were conducted using Spearman’s rank correlation coefficient specifically in relation to violence. This analysis revealed a significant negative correlation between age and violence score (p = –0.226, p = 0.038), suggesting that older individuals tend to report less violence.

Table 3 Table 3 Correlation Between Age, Education, Income, Vulnerability, Resilience, Overall Life Satisfaction, Satisfaction Referenced to Domains, and Mood State in Relation to the Total Sample (n=85) Pearson's R Notes.

No. people = Number of people in the household; FDI = Family Development Index; CD-RISC = Connor-Davidson Resilience Scale; Overall S. = Overall life satisfaction; SRD = Satisfaction referenced to domains; MS = Mood state^*p < 0.05, ^**p < 0.01, ^***p < 0.001

As the division into categorical groups can lead to a loss of information, vulnerability was considered a continuous variable to make the analysis more accurate. The Multivariate Analysis of Variance test considered age and FDI as continuous predictor variables, while Overall life satisfaction, Mood, and Satisfaction referenced to domains (SWB dimensions) were considered dependent variables. The results showed an effect of age (F (3,79) = 2.98; p = 0.036; η²p = 0.102) on SWB and also an effect of FDI (F (3,79) = 4.514; p = 0.006; η²p = 0.146) on SWB.

Based on the correlations and the results of the Analysis of Variance, a trend was observed that age positively influenced SWB measures, meaning that as age increased, the SWB dimension scores tended to increase. The FDI scores also positively influenced SWB values, so as vulnerability decreased, SWB scores tended to increase. Apparently, the influence of age on SWB is more related to SRD, while the influence of FDI on SWB is related to Mood State. In addition to vulnerability affecting SWB, it also affects resilience (CD-RISC), as positive correlations were found between resilience and FDI (r = 0.24; p = 0.024). Accordingly, as vulnerability decreases, resilience also increases. Therefore, individuals with a higher degree of vulnerability would tend to be less resilient and have lower subjective well-being.

To further explore these associations, we tested whether resilience mediated the relationship between vulnerability and the three SWB dimensions. Significant correlations among FDI, CD-RISC, and MS enabled the analysis of resilience as a mediator between vulnerability and mood state. The results indicated a trend toward partial mediation by resilience (indirect effect = 0.344; p = 0.081), with a significant total effect (total effect = 1.557; p = 0.004) and a still significant direct effect (direct effect = 1.213; p = 0.023). Resilience accounted for approximately 22.1 % of the total effect. A moderation model was also tested, but the interaction was not significant (p = 0.558).

In the case of Satisfaction referenced to domains (SRD), no significant direct correlation was found with FDI. However, significant correlations were observed between FDI and CD-RISC, and between CD-RISC and SRD. The mediation analysis revealed a significant indirect effect of resilience (indirect effect = 0.557; p = 0.049), which accounted for 80.5 % of the total effect. The direct effect of vulnerability on SRD was not significant (p = 0.827), suggesting full mediation.

For Overall life satisfaction, no significant correlations were found between the variables, and the mediation analysis did not show significant indirect (p = 0.246) or direct (p = 0.274) effects.

A close relationship was found between vulnerability and other variables. Strong positive correlations were identified between FDI and education (higher vulnerability was correlated with lower education) (r = 0.71; p < 0.001) and FDI and income (r = 0.45; p < 0.001). Regarding vulnerability (FDI), a moderate negative correlation was found between age and FDI (r = -0.31; p = 0.004), with lower degrees of vulnerability equating to lower age; a moderate positive correlation between FDI and income, with the degree of vulnerability increasing as income decreased (r= 0.45; p < 0.001); and weak positive correlations between Mood and FDI (r = 0.29; p = 0.005).

This study examined the associations between social vulnerability, resilience, subjective well-being (SWB), and exposure to violence in older adults. Some initial hypotheses were only partially supported. Greater social vulnerability, as indicated by lower FDI scores, was associated with reduced resilience and poorer SWB, particularly regarding mood. In contrast, violence showed only weak associations with specific dimensions of SWB and was not significantly related to vulnerability or resilience in the present sample.

A central finding of this study concerns the role of social vulnerability as a multidimensional contextual condition influencing psychological adaptation in later life. Although previous studies have often examined isolated socioeconomic indicators such as income or education, the present study considered vulnerability as an integrated construct encompassing access to knowledge, work opportunities, material resources, housing conditions, and family composition. The results suggest that these dimensions operate cumulatively, shaping the resources older adults can mobilize to cope with adversities. In this sense, vulnerability may function not only as a socioeconomic condition but also as a structural context influencing resilience processes and subjective well-being.

The findings also contribute to the literature by clarifying the relationship between vulnerability and resilience. Previous research has reported inconsistent associations between resilience and individual socioeconomic indicators (e.g., Fortes et al., 2009; Górska et al., 2022). By contrast, the present study suggests that broader contextual conditions may be more relevant for understanding resilience in aging. When vulnerability is considered as a multidimensional construct, its association with resilience becomes more apparent, supporting theoretical perspectives that conceptualize resilience as a dynamic process emerging from the interaction between individuals and their environments (Smith & Hayslip, 2012).

This study also identified a negative association between social vulnerability and mood (the emotional component of SWB) such that greater vulnerability corresponded to more negative than positive affect. Other studies that analyzed isolated economic variables found similar results (Martins et al., 2021; Muhammad et al., 2021; Reyes et al., 2020). The effect of social vulnerability on SWB observed in the present study offers a broader perspective, since vulnerability encompasses multiple sociodemographic and contextual variables. Notably, few studies have examined social vulnerability as an integrated construct; most research has focused instead on isolated indicators such as income or education (Cachioni et al., 2017; Guedea et al., 2006; Reyes et al., 2020). This broader approach underscores the importance of conceptualizing vulnerability multidimensionally to better capture its impact on subjective well-being.

Another important finding of the present study concerns the heterogeneous pattern observed across different components of subjective well-being. While resilience was involved in indirect associations linking social vulnerability to certain components of subjective well-being (mood state and domain-referenced satisfaction), constituting a potential explanatory pathway, no mediation effect was observed for global life satisfaction. This result reinforces the idea that subjective well-being is not a homogeneous construct. The judgments people make about their lives as a whole, or about specific domains, involve a comparison with the standards they set for what constitutes a good life. In contrast, when they report pleasant or unpleasant emotions (positive and negative affect), they are responding to present events and circumstances linked to contextual experiences and conditions. These two components therefore refer, respectively, to judgment versus experience (Diener et al., 2018). As a result, psychological resources such as resilience may exert a stronger influence on these more proximal dimensions of well-being (mood state and domain-referenced satisfaction) than on global evaluations of life satisfaction.

Another hypothesis, possibly more parsimonious, is that vulnerability may not exert a direct effect on satisfaction referenced to domains (SRD). Instead, its role may lie primarily in providing the conditions under which resilience develops, given that resilience typically emerges in response to adversity. From this perspective, part of the effect of resilience on SRD may stem specifically from adversities that have been successfully overcome. In other words, it may not be conditions of vulnerability themselves that influence satisfaction with different life domains, but rather the process of coping with and overcoming these conditions, which is reflected in levels of resilience and, through this pathway, becomes associated with more positive evaluations of these domains. Future research should more carefully examine the relationship between the components of SWB, resilience, and social vulnerability.

These findings contribute to ongoing discussions regarding the scope of the resilience construct in later life. Although resilience is often conceptualized as a key resource for maintaining well-being under adverse conditions (Fan et al., 2023; Fontes & Neri, 2015; Martins et al., 2021), the present results suggest that its influence may be more pronounced in emotional regulation and perceptions of functioning in specific life domains than in global cognitive evaluations of life satisfaction. This distinction highlights the possibility that resilience operates primarily through mechanisms related to coping with daily challenges and maintaining emotional balance rather than shaping broader life appraisals. Future research should delve deeper into the role of resilience as a mediator or moderator of SWB in relation to other variables in later life, as other authors have suggested (Bhattarai et al., 2021; Chen, 2016).

Finally, the role of violence in the explanatory model was more limited than initially expected. Although violence is widely recognized as a major risk factor affecting the health and well-being of older adults (Machado et al., 2020; WHO, 2015), in this study it was associated only with specific SWB dimensions and did not emerge as a significant predictor of resilience or vulnerability. This result should be interpreted with caution. Violence was examined as an exploratory variable, and the instrument used captured only the presence of different forms of abuse rather than their severity, chronicity, or relational context. It is therefore possible that more detailed assessments of violence would reveal stronger associations with psychological outcomes.

In this study, reports of violence were identified in both high- and low-vulnerability groups, with no statistically significant differences in overall scores, although the more vulnerable group exhibited higher mean levels. Among the more vulnerable participants, men and women reported violence in roughly equal proportions, whereas in the less vulnerable group, women reported higher rates than men.

This study identified a significant negative correlation between age and violence scores, indicating that older participants reported lower levels of violence. This finding may reflect increased vulnerability in advanced age, including greater dependence or social isolation, which can hinder the recognition or disclosure of violent experiences. Alternatively, underreporting may be explained by generational differences in the perception or tolerance of violent behaviors, or by fear of retaliation and institutional consequences. More broadly, comparisons across studies remain challenging, given the heterogeneity of methodological approaches (Barros et al., 2019) and the limited number of investigations with strong empirical evidence (Santos et al., 2020).

An important finding of this study was the prevalence of violence reported by 25.9 % of participants, a rate higher than global estimates provided by the WHO (2015). In Brazil, prevalence data remain imprecise due to underreporting and methodological inconsistencies across studies. For instance, Santana et al. (2016) reviewed six regional investigations and found prevalence estimates ranging from 3.2 % to 20.8 %, depending on the type of violence assessed and the methods employed.

Psychological violence emerged as the most frequent type, followed by financial violence, a pattern consistent with other Brazilian studies (Bolsoni et al., 2016; Santana et al., 2016). The occurrence of violence against older adults has been linked to a wide range of factors, including income, education, family structure, social support, and functional dependence (Barros et al., 2019; Bolsoni et al., 2016; Santos et al., 2020). Such findings highlight the multidetermined nature of this phenomenon and the importance of considering interacting variables. Future research should adopt integrative approaches that capture these complexities, thereby informing the development of effective prevention strategies within public policy frameworks.

This study presents limitations that must be acknowledged. The non-random and relatively small sample restricts the generalizability of the conclusions. Additionally, categorizing participants only as high or low in social vulnerability may have obscured more nuanced variations, given the diversity of social contexts. Future research should use larger samples and treat vulnerability as a continuum to better capture this variability. It is also important to examine other relevant variables, such as social support or perceived health, that may mediate the effects of vulnerability on resilience, well-being, and violence. Broadening the range of variables studied will allow for a more comprehensive understanding of these multidetermined constructs. Another limitation concerns the use of cross-sectional data to estimate mediation models. Without temporal precedence, it is not possible to establish the directionality of the relationships observed, and alternative models (e.g., reverse or reciprocal associations) cannot be ruled out. In addition, future research should investigate violence among older adults using instruments that differentiate types of violence in terms of severity, chronicity, and relational context. Such differentiation would allow for a more precise understanding of how violence interacts with social vulnerability and resilience processes in shaping subjective well-being.

In conclusion, although several of the associations observed in this study are consistent with previous literature, the findings also invite a more nuanced interpretation of the relationships between social vulnerability, resilience, and subjective well-being in later life. First, the results highlight the importance of conceptualizing social vulnerability as a multidimensional construct rather than relying on isolated socioeconomic indicators. By integrating multiple contextual dimensions, the present study suggests that structural inequalities may influence resilience and well-being through cumulative mechanisms that are not captured when variables such as income or education are examined separately.

Second, the heterogeneous pattern observed across the components of subjective well-being raises important questions regarding the scope of the resilience construct. While resilience mediated the relationship between vulnerability and affective and domain-specific aspects of well-being, it did not explain global life satisfaction. This finding suggests that resilience may operate primarily through mechanisms related to emotional regulation and everyday functioning, rather than shaping broader cognitive evaluations of life as a whole. Such distinctions underscore the need to treat subjective well-being as a multidimensional phenomenon when investigating psychological adaptation in aging.

Finally, the relatively limited role of violence in the explanatory model highlights the complexity of its relationship with well-being in later life. Although violence is widely recognized as a significant risk factor for older adults, its effects may depend on contextual factors such as severity, chronicity, relational dynamics, and social support. Future studies should adopt more detailed conceptual and methodological approaches to better understand how experiences of violence interact with social vulnerability and resilience processes across the aging trajectory.

Taken together, these findings suggest that future theoretical models of aging should integrate structural vulnerability, psychological resources, and interpersonal stressors within a more comprehensive framework. Advancing this agenda will require longitudinal designs, multidimensional measures of vulnerability, and more refined assessments of well-being and violence. Such developments may contribute to a deeper understanding of how older adults maintain psychological adaptation in contexts marked by social inequality.