Population aging is a worldwide phenomenon that has been occurring globally since the 19^th century, however, in low- and middle-income countries like Brazil, it has intensified since the 1970s (Carvalho & Garcia, 2003). In Brazil, people aged 60 years or over currently represent 15.6 % of the population, an increase of 56.0 % compared to 2010 (Instituto Brasileiro de Geografia e Estatística [IBGE], 2022). However, the aging process is not homogeneous across the population and reflects various types of inequalities, such as gender, race, and income. Throughout life, these inequalities have a cumulative impact on health, as poorer older adults generally have more health problems and difficulties accessing services (World Health Organization [WHO], 2015). Accordingly, individuals in unfavorable conditions are in a situation of vulnerability.

Vulnerability is a multidimensional and dynamic construct (Ayres et al., 2006) that applies to the condition of individuals or groups whose capacity for self-determination is reduced, associated with a notion of incapacity, lack of resources, and loss of autonomy (Barchifontaine, 2006; Tedesco & Liberman, 2008). Individuals in this condition have difficulties protecting their own interests since they lack power, education, resources, and sufficient strength (WHO, 2002). Vulnerability takes different forms. Individual vulnerability, according to Ayres et al. (2006), is determined by biological, cognitive, and behavioral conditions. Social vulnerability refers to contextual conditions that define individual vulnerability, involving access to media, schooling, availability of material resources, the power to participate in political decisions, and defense against violent coercion. Finally, programmatic vulnerability refers to policies and actions developed by public institutions aimed at protecting the individual. In this sense, the individual and collective dimensions would be intrinsically related.

Social vulnerability is characterized by exposure to risk factors that, combined, affect different dimensions of the well-being of individuals, families, or communities and impact these groups in various ways and with differing degrees of intensity (Jesus et al., 2017). It results from structural factors of society, such as poverty or lack of access to education and services, and is related to exclusion, discrimination, and weakening of social groups (Rinco et al., 2012). Social vulnerability differentially impacts older individuals in various aspects, including physical frailty (e.g., Jesus et al., 2017), quality of life, and perception of family support (Inouye et al., 2010).

Social vulnerability can create situations that require older adults to have the ability to face adversities, tragedies, threats, or sources of stress while maintaining adaptive behavior in the face of difficulties, which has been referred to in the literature as resilience (American Psychological Association [APA], 2020; Phillips et al., 2016). Resilience is considered a complex process resulting from interactions between the individual and the environment, involving risk factors (individual, environmental, and cumulative) and protective factors, each increasing or decreasing the likelihood of a resilient outcome (Smith & Hayslip, 2012).

Many studies have examined correlations between resilience and other variables in older adults (e.g., religion - Silva et al., 2019, cognitive performance - Fortes et al., 2009, and depressive symptoms - Fontes et al., 2015). Although previous studies have examined associations between socioeconomic conditions, resilience, and subjective well-being in older adults, most investigations have relied on isolated indicators such as income or education. A meta-analysis conducted by Górska et al. (2022), for example, identified that income and education are variables that have a weak correlation with resilience. The same conclusion was drawn in a Brazilian study conducted by Fortes et al. (2009). Phillips et al. (2016) evaluated the relationship between resilience and older adults' perception of their income sufficiency in a longitudinal study conducted in different countries (Canada, Colombia, Brazil, and Albania). The study found that individuals who perceived their income as insufficient had lower levels of resilience.

Less attention has been given to social vulnerability as a multidimensional construct that integrates several contextual conditions simultaneously. Examining vulnerability in this broader sense may provide a more comprehensive understanding of how structural inequalities shape resilience processes in later life. Since resilience is a dynamic process (not a fixed personality trait), vulnerability could be a cumulative risk factor in the lives of older adults, impacting resilience. The present study contributes to the literature by investigating whether a multidimensional measure of social vulnerability can help clarify the mechanisms linking contextual disadvantage, resilience, and subjective well-being among older adults, particularly in a middle-income country context.

Associated with the individual's ability to cope with stressful events throughout life, subjective well-being (SWB) could protect older adults from the influence of losses, risks, and threats to their adaptation to such adverse events (Fontes & Neri, 2015). Subjective well-being is an individual's evaluation of their own life regarding emotional components (positive and negative affects) and cognitive components (life satisfaction) (Diener et al., 1999). Positive and negative affects refer to emotions and mood. Lucas et al. (1996) define life satisfaction as an overall assessment made by an individual about their life, based on a consideration of positive and negative aspects. In this sense, individuals exhibit high SWB when they experience more pleasant than unpleasant emotions and are satisfied with their own lives (Diener, 2000).

Research on SWB in older people indicates that it correlates positively with subjective religiosity (Cardoso & Ferreira, 2009), age (Cachioni et al., 2017), psychological well-being (Reyes et al., 2020), functional independence (Sposito et al., 2010), and self-esteem (Gomes et al., 2016). The study by Reyes et al. (2020), conducted with older adults in Colombia, also found that life satisfaction (a component of SWB) had a direct relationship with resilience and that socioeconomic status had a positive correlation with psychological well-being. The relationships found in the study by Reyes et al. (2020) suggest a possible relationship between SWB, resilience, and social vulnerability (at least concerning income), which still need to be confirmed in other cultures.

In later life, exposure to violence may represent an additional stressor capable of affecting both resilience and subjective well-being. Experiences of violence have been associated with a higher likelihood of mental health problems and lower subjective well-being, as well as compromising older adults’ sense of security and autonomy (Srivastava & Muhammad, 2020). From a theoretical perspective, violence can be understood as a contextual stressor that interacts with structural vulnerability conditions and may influence the psychological resources available to cope with adversity. However, there are few empirical studies that simultaneously examine social vulnerability, resilience, subjective well-being, and violence.

Different forms of violence can affect the physical and mental health of older adults. The World Health Organization (WHO, 2015) defines violence against older people as single or repeated acts, or the absence of appropriate actions, occurring within a relationship of trust that cause harm, suffering, or distress. The Ministério da Saúde (2007) classifies such violence as physical, sexual, psychological, economic, institutional, abandonment/neglect, and self-neglect. In high- and middle-income countries, the prevalence of overall violence against older adult’s ranges from 2.2 % to 14.0 %, including financial (1.0 – 9.2 %), psychological (0.7 – 6.3 %), and physical violence (0.2 – 4.9 %) (WHO, 2015). Although prevalence estimates vary across studies, available evidence indicates that a substantial proportion of older adults experience some form of abuse or mistreatment, making this a relevant public health concern.

Understanding whether and how violence is related to resilience and SWB is relevant as it allows for a better understanding of how violence can affect older people's adaptation to adversities and impact their subjective well-being. There are still few studies that have systematically investigated the relationship between the variables listed here among adults over 60 years of age.

The present study aimed to examine the relationships between social vulnerability, resilience, subjective well-being, and experiences of violence in a sample of adults aged 60 years or over. More specifically, the study investigated whether social vulnerability, conceptualized as a multidimensional contextual condition, is associated with resilience and subjective well-being, and whether resilience functions as a mechanism linking vulnerability to well-being outcomes. In addition, the study also explored whether experiences of violence were associated with these constructs. Given the limited empirical evidence addressing these relationships simultaneously, the analysis involving violence should be interpreted as exploratory.

Based on the literature revised, one hypothesis of this study is that older adults experiencing greater social vulnerability will exhibit lower levels of resilience and subjective well-being, encompassing both the cognitive dimension (global life satisfaction) and the affective dimension (positive and negative affect). Furthermore, it was expected that that resilience would partially mediate the relationship between vulnerability and subjective well-being. In addition, it is hypothesized that participants who have been exposed to violence will demonstrate lower resilience and SWB scores, and that resilience will function as a protective factor, buffering the negative impact of violence on SWB.

Method

Participants

Data were collected from 98 participants. Four participants were part of the pilot study, and the data from 13 participants were eliminated from the analysis for various reasons (withdrawal, not meeting the minimum age requirement, reporting medical treatment for memory complaints, and difficulty understanding the questions). The final sample consisted of 85 participants. Some participants with less social vulnerability were recruited using the snowball technique, based on personal contacts of the researchers. The other participants were recruited directly (personal approach) at a public Geriatrics outpatient clinic, and at their homes, with the assistance of community health agents from four Family Health Strategy Units in low-income neighborhoods. Exclusion criteria were: being under 60 years of age, having an uncorrected hearing impairment that prevented understanding of the questions, having a diagnosis of a neurocognitive disorder, or being under treatment for memory problems (indicative of cognitive decline). All participants signed a consent form, and the research was approved by the Ethics Committee for Research with Human Subjects of the Faculty of Philosophy, Sciences, and Letters of Ribeirão Preto – University of São Paulo, under authorization No. CAAE 57342822.2.0000.5407.

Instruments

Identification Interview: Consisting of 31 questions that assessed the participant's general health status, education level, family situation, and social support network. These questions allowed for the investigation of the vulnerability status, according to the Family Development Index.

Family Development Index (FDI): This instrument assesses the degree of vulnerability of the participants. It comprises six dimensions, 26 components, and 48 indicators, with the family being the unit of analysis. The FDI score ranges from 0 (families in the worst possible development situation) to 1 (families in the best possible development situation). The six dimensions evaluated by the FDI are the absence of vulnerability, access to knowledge, access to work, availability of resources, child development, and housing conditions (Carvalho et al., 2021).

Connor-Davidson Resilience Scale (CD-RISC): This unifactorial scale contains 25 items and evaluates individual resilience (Connor & Davidson, 2003). Lopes and Martins (2011) analyzed the psychometric properties of the Brazilian version of the scale. The scale obtained a Cronbach's alpha of 0.82. Each item is scored from 0 (not at all true) to 4 (almost always true), so the total resilience score ranges from 0 to 100 points, with higher scores indicating greater resilience (Solano, 2016).

Subjective Well-Being (SWB): Composed of three distinct instruments. The scale for measuring Global Life Satisfaction consists of a single item represented graphically by a ladder, asking the participant to rate their life satisfaction at that moment from 1 (the first step of the ladder, representing the worst possible life) to 10 (the top step of the ladder, representing the best possible life) (Cachioni, 2012). The instrument for measuring Referenced Life Satisfaction evaluates how satisfied the individual is with their health and physical ability, mental capacity, and social involvement, and compares satisfaction in these domains with other people of the same age. The instrument comprises eight statements to be rated on a scale from 1 (very dissatisfied) to 5 (extremely satisfied); the closer the total mean score is to 5, the greater the satisfaction in these domains (Cachioni, 2012). Finally, the Positive and Negative Affect Scale (PANAS) evaluates the participant's daily feelings, rating the intensity of each feeling from 1 (not at all) to 5 (extremely). Positive affect includes six emotions (happy, joyful, enthusiastic, well, satisfied, content) and negative affect includes eight emotions (irritated, unmotivated, distressed, depressed, upset, nervous, sad, discouraged). The closer the mean score is to 5, the better the day-to-day mood (Siqueira et al., 1999).

Questionnaire for Identifying Violence Against Older Adults: Evaluates potential situations of violence against older adults in the realms of physical violence, psychological violence, and financial and economic abuse, as adopted by the Ministry of Health in the Primary Care Booklets (Ministério da saúde, 2007). The questionnaire has seven items, and for each "yes" response to a type of violence experienced, one point was assigned for statistical analysis purposes. The scale scores range from 0 to 7, with individuals scoring 0 reporting no violence, and those scoring from 1 to 7 reporting some type of violence; higher scores indicated a greater variety of violence experienced.

Procedures

All interviews were conducted in a private setting, either at the participants' homes or in locations provided by the health units or Family Health Strategy units. After signing the consent form, data collection began with the identification interview, which allowed for the assessment of the participant's social vulnerability status according to the FDI. Subsequently, the CD-RISC Scale and the Subjective Well-Being (SWB) measures were administered. Lastly, the questionnaire for identifying violence against older adults was applied. In cases where violence was identified, the researcher provided guidance to the participants, presenting the necessary information for them to report the violence and seek help.

Data analysis

All instruments were scored according to the instructions in their manuals, and the statistical analyses were performed using IBM SPSS Statistics software (IBM Corp., 2015). Initially, exploratory analyses were conducted to examine the descriptive measures and data distribution. The Shapiro-Wilk test revealed that only the scores for Mood State (p = 0.25; W= 0.98) and Domain-Referenced Satisfaction (p = 0.09; W = 0.97) followed a normal distribution, while the data for age, education, income, vulnerability (FDI), resilience (CD-RISC), Overall life satisfaction, violence score, and number of people in the household did not present normal distributions (p < 0.05). Nonetheless, given the relatively large sample size, parametric tests were used for these measures based on the central limit theorem (Morettin & Bussab, 2017).

For inferential analyses, Pearson’s correlations were first performed among the research variables, following Cohen's suggestion for classifying the strength of these correlations (Cohen, 1988, as cited in Figueiredo & Silva, 2009, p. 119). Next, analyses were conducted to examine differences between groups with more and less social vulnerability. To compare independent groups, Student's .-test was used for the variable "number of people in the household," and Welch's .-test was used for the variables age, education, and income, since the assumption of homogeneity of variances was not met. The independent variable considered was the degree of vulnerability (high/low).

Considering vulnerability as a continuous variable, a Multivariate Analysis of Variance (MANOVA) was performed, given that the SWB instrument comprises three dimensions, resulting in a score for each dimension. Accordingly, the multivariate analysis allowed SWB to be considered a phenomenon composed of the three dependent variables. As there was no multivariate normality, Pillai's Test was chosen, as it is a more robust test for deviations from normality. In this multivariate analysis, the constructed model did not violate the assumption of no multicollinearity, and a multivariate outlier based on the Mahalanobis distance was found and removed.

The post hoc sample calculation was performed using the G*Power version 3.1 software (Faul et al., 2009), considering a correlation value of 0.3, α of 0.05, and n = 85. The observed statistical power was 0.80. For the multivariate analysis, the post hoc sample calculation was obtained using the IBM SPSS Statistics (IBM Corp., 2015) software and observed powers of 0.68 for age and 0.86 for FDI were identified.

Results

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.

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.

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.

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).

Discussion

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.

References

Notes

^* Research article.

Author notes

a Correspondence author. Email: aschmidt@ffclrp.usp.br

Additional information

How to cite: Zanin, M. F., Manfredo, L. C., & Schmidt, A. (2026). Resilience, subjective well-being and violence in older adults with different levels of social vulnerability. Universitas Psychologica, 25, 1-15. https://doi.org/10.11144/Javeriana.upsy25.rswb

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