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Parents of children in need of care, such as those caring for chronically ill and disabled children, are exposed to significant stress associated with caregiving, placing them at risk for mental disorders. Resilience factors, as psychological resources, can help mitigate the negative effects of stress for both parents and their children, ultimately promoting resilient outcomes. However, little is known about the relationship between resilience factors and resilient outcomes in this highly stressor-exposed population. The aim of this study was to investigate the relationship between resilience factors and resilient outcomes in parents of children in need of care, thereby contributing to a better understanding of how these factors can influence parents’ quality of life. A sample of 202 German-speaking parents of children in need of care from a non-randomized controlled trial (ID: NCT05418205) completed measures assessing resilience-related outcomes, including indicators of mental distress, well-being, perceived stress, and the ability to recover from stressors. Using k-means cluster analysis, two clusters were identified, differentiating burdened and unburdened individuals based on their responses. Logistic regression was subsequently conducted to examine the predictive role of psychological resilience factors—self-efficacy, social support, optimism, internal locus of control, and family cohesion—in distinguishing between the two groups. Results from the logistic regression analysis revealed that self-efficacy, social support, optimism, and family cohesion were significant predictors of cluster membership. These findings contribute to the understanding of the influence of resilience factors on resilient outcomes in parents of children in need of care.
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In the European Union, approximately 4% of the children experience moderate to severe activity limitations due to health problems (Biasci et al., 2019). The majority of these children are cared for at home, with the primary caregiving responsibility falling on one family member, typically the mother (Oetting-Roß, 2022). Parents of children in need of care often face unique challenges and are exposed to significant stress in relation to their caregiver role (Keller & Honig, 2004; Majnemer et al., 2012; Page et al., 2020). These challenges include financial strains as well as emotional and physical burdens (Raina et al., 2004). Other strains derive from the limited availability and accessibility of support services and difficulties navigating the complex healthcare system (Kofahl et al., 2017; Oetting-Roß, 2022). This increases the risk of health, social and economic impairments for families living with disabled or chronically ill children and thus, often results in poorer living conditions and care situations (Jenessen, 2022).
Responses to stress are heterogeneous. While some parents show elevated levels of mental distress or manifest mental disorders, others are able to maintain or quickly regain their mental health even in face of significant stress, that is, they remain resilient (Ilias et al., 2018; Lin et al., 2013; Mohan & Kulkarni, 2018; Ye et al., 2017). Psychological resilience describes the dynamic process of maintaining or quickly regaining mental health in psychologically and/or physically stressful situations (Kalisch et al., 2015, 2017). Thereby, resilience as an outcome does not necessarily reflect an insensitivity to stress, but rather the result of an ongoing active and dynamic adaptation process. This contrasts with previous definitions of resilience as a rather stable personality trait (for a critical discussion see: Bonanno, 2021; Kalisch et al., 2015). The trait approach to resilience is now seen as limited because it tends to overlook the changing and contextual ways in which individuals respond to challenges (Southwick et al., 2014). Recent research suggests that resilience is better understood as a process influenced by both internal and external factors, rather than as a fixed trait (Kalisch et al., 2024). Over time, resilience has been linked to better long-term mental health outcomes, such as lower rates of depression and anxiety, and well-being (Schäfer et al., 2022; van der Meulen et al., 2020). However, many of the protective factors (so-called resilience factors) discussed in current models of resilience - such as biological, psychological and social resources - are closely related to what was previously conceptualized under the heading of ‘trait resilience’ (Kalisch et al., 2015; Ungar & Theron, 2020). Resilient outcomes are promoted by a variety of biological, psychological, and social resources (Feder et al., 2019; Kalisch et al., 2015). These resilience (or protective) factors help to cope with stressful life events and to mitigate risk factors (Mancini & Bonanno, 2009; Veer et al., 2021). Resilience factors have the potential to influence resilient outcomes, such as the preservation or restoration of mental well-being in response to stressors, by operating through higher-level resilience mechanisms like positive appraisal styles or regulatory flexibility, as indicated by previous research (Bonanno, 2021; Kalisch et al., 2015). Consequently, these resilience factors represent promising targets for interventions aimed at promoting and enhancing resilience. Resilience factors frequently discussed in the literature to promote mental health in adults are cognitive and behavioural coping strategies, forgiveness, mindfulness, self-efficacy, social support, optimism, locus of control and sense of coherence (Helmreich et al., 2017; Kaya & Odacı, 2024; Schäfer et al., 2022).
In resilience research, there is currently no universally accepted or standard measure of resilience (Bonanno, 2021; Kalisch et al., 2017). Therefore, there is an increasing interest in multidimensional resilient outcomes in resilience research – including mental health, well-being and stress (Infurna & Luthar, 2018; Schäfer et al., 2022). This claim is supported by preliminary evidence suggesting that the importance of unique factors may vary between different resilient outcomes (Schäfer et al., 2023).
As caregiving for children is a major stressor for families, it is of high importance to understand what allows parents to respond resiliently, that is, to maintain their mental health and, in turn in terms of functional health, their ability to care for their children. While a large body of research has demonstrated a positive link of resilience factors and resilient outcomes (i.e., low levels of mental distress, or high levels of wellbeing) for the general population (Liu et al., 2020) or children (Dray et al., 2017) less research has been done on this relationship for parents of children in need of care. A meta-analysis on studies of caregivers of children with developmental disabilities identified a negative relationship between resilience and psychological distress, as well as between resilience and anxiety, depression, and stress (Iacob et al., 2020). In a study of adolescents with disabilities, higher resilience was shown to be related to higher quality of life among adolescents and their parents (Migerode et al., 2012). Resilient outcomes in parents of children in need of care have shown positive effects on their relationship with their children (Gavidia‐Payne et al., 2015). Furthermore, a recent study found resilient outcomes in parents (i.e., higher wellbeing) to be positively associated with higher wellbeing in their children (Widyawati et al., 2022). Several studies have examined resilience factors in parents of children in need of care. In a review on stress and resilience in parents of children with intellectual and developmental disabilities, Peer & Hillman (2014) identified a problem-focused coping style, optimism and social support as important resilience factors. A cross-sectional study found social support and family strength to be relevant resilience factors (Toledano-Toledano et al., 2021). During the COVID-19 pandemic, Montirosso et al. (2021) explored resilience factors in parents of children with neurodevelopmental disabilities and identified optimism, self-efficacy, and family cohesion as important factors.
Aim and objective of the study
The purpose of our studies is to examine the relationship between resilience factors and resilient outcomes among parents of children in need of care. Specifically, we investigate whether higher levels of resilience factors contribute to a reduced likelihood of experiencing high burden (i.e., less resilient outcomes). By understanding specific resilience factors that are associated with lower levels of burden, we derive valuable insights for the development of interventions and support strategies aimed at promoting resilience and alleviating burden in parents caring for chronically ill and disabled children.
Method
Study design and sample recruitment
The data utilized in this study were collected during the pre-intervention baseline assessment conducted between February 2, 2021, and September 1, 2022, as part of the NEST research project in Germany (Nickel et al., 2023). The NEST project (Strengthening and relief for families with children in need of care through family health partners in regional network structures) is designed to evaluate the effectiveness of an intervention for families with children in need of care using a non-randomized controlled trial design. The evaluation of the NEST study involves a longitudinal online study with four measurement time points. Participants engaged in the study through the online platform SoSci Survey (Leiner, 2023), which facilitates data collection. While the project itself was prospectively registered on the German Clinical Trials Register (ID: DRKS00027465) as well as on ClinicalTrials.gov (ID: NCT05418205), the specific analyses performed in this study were not part of the registration.
A total of 204 participants were recruited from two established German support networks for parents of children in need of care, namely nestwärme e. V. and Kindernetzwerk e. V. To be eligible for the study, participants had to meet two inclusion criteria: (1) being a parent of a child with a disability and/or chronic disease based on the criteria outlined in German social law (benefit receipt according to § 37 of Germany’s Social Security Code V and/or level of care 1), and (2) being at least 18 years old. In the context of German social law, the care level (“Pflegegrad”) is a classification system used to determine the level of assistance a person requires due to health-related needs or disabilities. Care levels range from 1 (lowest) to 5 (highest), with level 1 representing a mild need for care and level 5 indicating severe dependence on assistance for daily activities. For a comprehensive description of the inclusion and exclusion criteria, please refer to Nickel et al. (2023). The sample size planning for the NEST project was based on the planned intervention; however, no separate sample size planning was conducted for the specific analyses presented in this paper. The study was approved by the Local Ethics Committee of the University Medical Center Hamburg-Eppendorf (application number: LPEK-0370). All respondents provided informed consent according to the Declaration of Helsinki and its latest revisions.
As no intervention had been implemented at the baseline assessment, there were no substantial differences observed between the intervention and control group in terms of the key predictors and outcomes examined in this study. Consequently, data from both groups were combined to enhance statistical power and enable comprehensive analyses for the present investigation.
Measures
The online survey included the following sections: sociodemographic data, access to and use of utility services, resilient outcomes, resilience factors and stressor load (for more details, see Nickel et al., 2023). Except for the assessment of sociodemographic data, only validated questionnaires were used.
In this manuscript, we focus on the following outcomes and risk/protective factors; other results will be published elsewhere.
Sociodemographic
Sociodemographic characteristics included items on participants’ age, gender, marital status, and education. Moreover, characteristics of the affected children were assessed, i.e., age, gender, and level of care. In Germany, levels of care (German “Pflegegrad”) represent a classification system used to determine the degree of functional and mental impairments due to a chronic illness or disability. The system has five levels, with level 1 being the lowest and level 5 being the highest and is used to determine the amount of financial assistance and support a person can receive for their care needs (Glazinski et al., 2020).
Resilient outcomes
Wellbeing was assessed using the WHO-Five Well-Being Index (WHO-5; Brähler et al., 2007). The WHO-5 consists of five items on a 6-point Likert scale. The total score of the WHO-5 measurement instrument is calculated as the sum score, obtained by adding up the raw scores of the individual items. Higher values indicate higher wellbeing. In our sample the internal consistency, measured using McDonalds’s ω, was high ω = 0.91, 95% CI [0.91; 0.96]. This is comparable to the reliability found in the validation study (Brähler et al., 2007), which reported an α of 0.92. Mental distress was measured using the 28-item version of the General Health Questionnaire (GHQ-28; Klaiberg et al., 2004). The GHQ-28 assesses somatic symptoms, anxiety and insomnia, social dysfunction, and severe depression with 28 items on a 3-point Likert scale. The total score of the GHQ-28 is determined by summing the raw scores of all individual items, providing an overall measure of mental distress. Higher scores indicate more severe mental distress. In the present study, internal consistency was good, ω = 0.93, 95% CI [0.91; 0.94], which is comparable to the reliability reported in the validation study, where α was 0.92 (Klaiberg et al., 2004). Perceived stress was measured using the Perceived Stress Scale 4 (PSS-4; Klein et al., 2016), with 4 items on a 5-point Likert scale. The total score of the PSS-4 is derived by summing the raw scores of its individual items, yielding an overall measure of perceived stress. Higher scores indicate more severe stress. In the current study, internal consistency was acceptable, ω = 0.77, 95% CI [0.71; 0.82], which is comparable to the reliability found in the validation study, where α was 0.75 (Klein et al., 2016). Stressor recovery ability (also referred to as self-reported resilience) was assessed using the Brief Resilience Scale (BRS; Chmitorz et al., 2018). As the ability to recover from stress is central to resilience, it can be considered a good proxy for resilience (Chmitorz et al., 2018; Rodríguez-Rey et al., 2016). The BRS has 6 items that are rated on a 5-point Likert scale. The total score of the BRS is calculated as the sum score across individual items.
Higher scores indicate higher levels of stressor recovery ability. In the current study, the internal consistency was high, ω = 0.86, 95% CI [0.82; 0.89], which is consistent with the reliability reported in the validation study, where ω was 0.85 (Chmitorz et al., 2018).
Resilience factors
Self-efficacy was assessed using the General Self-Efficacy Short Scale-3 (ASKU, Beierlein et al., 2014), with 3 items on a 5-point Likert scale. The total score for the BRS is calculated by averaging the scores of the items. Higher scores indicate higher self-efficacy. In the present study, internal consistency was high, ω = 0.89, 95% CI [0.86; 0.92], which aligns with the reliability reported in the validation study, where ω was 0.87 (Beierlein et al., 2014). Social support was measured using the Oslo Social Support Scale (OSSS-3; Kocalevent & Brähler, 2013), with 3 items on a 4- resp. 5-point Likert scale. The total score for the OSSS-3 is calculated by summing the scores of the three items. Higher values indicate higher social support. In the current study, the internal consistency was acceptable, ω = 0.65, 95% CI [0.57; 0.73], which is comparable to the reliability reported in the validation study, where α was 0.64 (Kocalevent & Brähler, 2013). Optimism was assessed using the Optimism–Pessimism Short Scale–2 (SOP-2; Kemper & Beierlein, 2014), with 2 item being rated on a 7-point Likert scale. The total score for the SOP–2 is calculated by averaging the scores of the items. Higher values indicate higher optimism. In the present study, the internal consistency was high, ω = 0.89, 95% CI [0.82; 0.83], which is consistent with the reliability reported in the validation study, where ω was 0.83 (Kemper & Beierlein, 2014). Internal locus of control was measured using the Internal–External Locus of Control Short Scale–4 (IE–4; Kovaleva et al., 2014), with 2 items being rated on a 5-point Likert scale. The total score for the IE–4 is calculated by summing the scores of the items. Higher scores indicate a stronger internal locus of control. Since the scale consisted of only two items, we applied the Spearman-Brown formula (Eisinga et al., 2013) instead of assessing internal consistency. The resulting reliability coefficient was 0.71, which was in line with the reliability coefficient 0.74 from the validation study (Kovaleva et al., 2014). Family cohesion was assessed using the subscale Family Cohesion (FC) from the Resilience Scale for Adults (RSA; Kaiser et al., 2019) with 6 items being rated on a 7-point Likert scale. Higher scores indicate higher family cohesion. In the present study, the internal consistency was acceptable, ω = 0.80, 95% CI [0.75; 0.83], which is slightly lower compared to the reliability reported in the validation study, where α was 0.90 (Kaiser et al., 2019).
Data analyses
Analyses were conducted using R version 4.2.1 (R Core Team, 2024) and the packages easystats (Lüdecke et al., 2022), tidyverse (Wickham et al., 2019), factoextra (Kassambara & Mundt, 2022), NbClust (Charrad et al., 2014b) and sjPlot (Lüdecke, 2022).
We calculated the means and standard deviations for the resilient outcomes and resilience factors in our sample. To assess how these values compared to those reported in the respective validation studies, we conducted one-sample Wilcoxon tests (Wilcoxon, 1945). This non-parametric test allowed us to evaluate whether the mean values in our sample significantly differed from the mean values reported in the validation studies (Siegel, 1956).
To investigate the associations between different patterns of resilient outcomes and specific resilience factors, a k-means cluster analysis was performed on the z-standardized resilient outcome variables, namely wellbeing, mental distress, perceived stress, and stressor recovery ability. k-means is one of the most efficient unsupervised classification algorithms, which attempts to find a user-specified number of clusters represented by their centroids. The algorithm facilitates the identification of distinct clusters representing unique profiles of resilient outcomes, grouping individuals with similar response patterns across these variables. To determine the optimal number of clusters in our k-means cluster analysis, we utilized the NbClust function from the NbClust Package (Charrad et al., 2014b). The NbClust Package offers a comprehensive set of indices and statistical criteria that aid in determining the most suitable number of clusters. These indices include the silhouette index (Rousseeuw, 1987), the Dunn index (Dunn, 1974), the Caliński–Harabasz index (Caliński & Harabasz, 1974), and others, which assess the quality and structure of the clustering solution.
Given that clustering algorithms typically lack an internal mechanism for handling missing values (Sinaga & Yang, 2020), we opted to exclude eight individuals from the dataset due to the presence of missing values on one or more of the outcome variables.
Cluster analysis was successful in dividing the individuals in our sample into two clusters, which can be interpreted as high and low burden individuals, respectively. To examine the relationship between these clusters and resilience factors, a logistic regression analysis was conducted. Persons with missing values were excluded from the regression analyses, resulting in the exclusion of a total of 8 cases. The analysis included the resilience factors of self-efficacy, social support, optimism, internal locus of control, and family cohesion as predictor variables. We controlled for the potentially confounding effects of age, gender, relationship status, and education, ensuring that the analysis accounted for these variables in assessing the influence of the resilience factors on cluster membership. Categorical variables, such as gender, relationship status, and education, were included in the regression analysis as dummy-coded variables. The analysis yielded regression coefficients, odds ratios, and their corresponding p-values, providing insights into the strength and significance of the associations between the resilience factors and the likelihood of belonging to each cluster. By incorporating these resilience factors into the logistic regression model, we aimed to identify which factors contributed significantly to the differentiation between the low-burdened and high-burdened clusters.
Results
Sample characteristics
The final sample included 202 participants (see Table 1 for sociodemographic characteristics). Almost all the participants were women (93%). Participants’ age ranged between 25 and 59 years (M = 41.63, SD = 6.75). The age of children of these parents ranged between 1 and 17 years (M = 7.58, SD = 4.35), with an approximately equal distribution of genders (45% female and 55% male). The care levels of the children varied, with a small percentage falling into care level 1 (4%), while more children were at care levels 2 (13%), 3 (31%), 4 (29%), and 5 (23%).
Table 1
Descriptive characteristics of parents and children in the sample
Overall (n = 202)
Parents
Age (years)
Mean (SD)
41.63 (6.75)
Median (Min; Max)
41.00 (25; 59)
Gender (frequency, %)
Female
187 (92.57%)
Male
15 (7.43%)
Marriage status (frequency, %)
Single
23 (11.39%)
Married
155 (76.73%)
Divorced
23 (11.39%)
Widowed
1 (0.50%)
Education (frequency, %)
No school-leaving qualification
1 (0.52%)
Low secondary education
13 (6.40%)
Medium secondary education
45 (22.17%)
High school
58 (28.57%)
University degree
70 (36.46%)
Other
5 (2.46%)
Children
Age (years)
Mean (SD)
7.58 (4.35)
Median (Min; Max)
7 (1; 17)
Gender (frequency, %)
Female
90 (44.55%)
Male
112 (55.45%)
Care level (frequency, %)
1
7 (3.66%)
2
25 (13.09%)
3
60 (31.41%)
4
56 (29.32%)
5
43 (22.51%)
n number of cases, SD standard deviation, Min minimum, Max maximum
Resilience factors and resilient outcomes
Table 2 presents the descriptive statistics for resilience factors and resilient outcomes.
Table 2
Scores obtained from instruments measuring resilient outcomes and resilience factors among respondents
Mean (SD)
Median (Min; Max)
ω, 95% CI
Resilient Outcomes
Well-being (WHO-5)
8.22 (5.06)
7.00 (0, 23)
0.91 [0.91; 0.96].
Mental distress (GHQ-28)
38.05 (13.28)
37.00 (12, 70)
0.93 [0.91; 0.94].
Perceived stress (PSS-4)
7.68 (3.62)
9.00 (0, 13)
0.77 [0.71; 0.82].
Stressor recovery ability (BRS)
3.04 (0.84)
3.00 (1, 5)
0.86 [0.82; 0.89]
Resilience Factors
Self-efficacy (GSE-3)
3.95 (0.72)
4.00 (1.67, 5.00)
0.89 [0.86; 0.92].
Social support (OSSS-3)
8.43 (2.11)
8.00 (4, 14)
0.65 [0.57; 0.73].
Optimism (SOP-2)
4.80 (1.48)
5.00 (1, 7)
0.89 [0.82; 0.83].
Internal locus of control (IE-4)
3.67 (0.82)
4.00 (1.5, 5)
0.711
Family cohesion (RSA)
29.36 (7.25)
29.00 (2, 47)
0.80 [0.75; 0.83]
n = 202;
SD standard deviation, Min minimum; Max maximum; ω Omega;
1spearman-brown coefficient
To assess the burden of parents, we compared the values of the resilience factors and resilient outcomes in our sample with those reported in corresponding validation studies. The results revealed that parents in our sample exhibited higher levels of mental distress compared to the general population, with empirically determined mean values of resilience factors falling below the reported mean values in the German validation studies. For a detailed overview of these comparisons, refer to Table SP 1 in the Supplementary Material. For correlations between the instruments, see Table SP 2 in the appendix.
Cluster analysis
The k-means cluster analysis was conducted using z-standardized outcome variables, including wellbeing, mental distress, perceived stress, and stressor recovery ability. The result of the NbClust function for determining the number of clusters indicated that among all the indices used, twelve suggested two as the optimal number of clusters, six suggested three, one suggested five, three suggested nine, and two suggested ten. The results of the individual indices are shown in Table SP 2 in the Supplementary Material. Therefore, we selected k = 2 as the optimal number of categories suited for our data and then used k-means clustering to cluster the data into two clusters. As shown in Fig. 1, Cluster 1 comprises 62 (31.47%) of the 197 clustered participants. On the other hand, Cluster 2 consists of 135 (68.53%) participants.
Fig. 1
Dimensionality reduction in k-Means clustering results. Note. The k-means clustering results show a high degree of similarity with regard to clustering designations. The clusters were generated using six resilience-related outcome variables, and to improve interpretability, these six dimensions were condensed into two principal components using principal component analysis (PCA) for dimensionality reduction. The two distilled axes, referred to as “Dim 1” and “Dim 2,” captured the largest amount of variance in the data (Kassambara, 2017)
×
The cluster analysis (see Fig. 1) revealed the presence of two distinct clusters, each exhibiting unique patterns of wellbeing, stressor recovery ability, mental distress, and stress perception. The results are presented in Table 3. The clusters collectively accounted for 39.80% of the variance in the original data. For a comprehensive overview of the differences between the two clusters, see Table SP 3 in the Supplementary Material.
Table 3
Mean values of z-standardized measures for the two clusters
Cluster 1 (n = 62)
Cluster 2 (n = 135)
Wellbeing (WHO-5)
1.02
−0.52
Mental distress (GHQ-28)
−0.85
0.44
Perceived stress (PSS-4)
−0.97
0.50
Stressor recovery ability (BRS)
0.86
−0.44
Cluster 1 demonstrated higher scores in wellbeing and stressor recovery ability, as well as lower scores in mental distress and perceived stress. Conversely, Cluster 2 exhibited different patterns of scores across these variables, suggesting a separate group with varying levels of wellbeing, resilience, mental distress, and stress perception. Cluster 1 can therefore be interpreted as a cluster of low-burden individuals. On the other hand, Cluster 2 can be interpreted as a cluster of highly burdened individuals.
Logistic regression
In the logistic regression analysis, we examined the relationship between cluster membership, which served as outcome and the resilience factors self-efficacy, social support, optimism, internal locus of control, and family cohesion as predictors. Table 4 presents the results of the logistic regression analysis.
Table 4
Logistic regression results for predictors of cluster membership
Odds ratio
SE
95% CI
z
p
Resilience factors
Self-efficacy
0.42
0.18
[0.17, 0.96]
−2.01
0.045
Social support
0.75
0.08
[0.21, 0.92]
−2.02
0.040
Optimism
0.67
0.13
[0.45, 0.96]
−2.12
0.035
Internal locus of control
0.57
0.19
[0.29, 1.06]
−1.74
0.083
Family coehsion
0.85
0.07
[0.72, 0.99]
−2.05
0.041
Control variables
Age
1.05
0.03
[1.00, 1.12]
1.58
0.115
Gender
0.57
0.48
[0.11, 3.07]
0.68
0.499
Relationship
1.47
1.06
[0.38, 6.63]
0.54
0.588
Education
0.67
0.23
[0.34, 1.29]
−1.17
0.241
SE standard error; CI confidence interval; z ratio of the estimated coefficient to its standard error
Several factors demonstrated varying degrees of influence on cluster membership. Notably, self-efficacy (OR = 0.42, 95% CI [0.17, 0.96], p = 0.045), social support (OR = 0.75, 95% CI [0.21, 0.92], p = 0.040), optimism (OR = 0.67, 95% CI [0.45, 0.96], p = 0.035), and family cohesion (OR = 0.85, 95% CI [0.72, 0.99], p = 0.041) exhibited statistically significant effects. The model’s explanatory power was substantial (Tjur’s R2 = 0.36).
In contrast, internal locus of control did not reach statistical significance (OR = 0.57, 95% CI [0.29, 1.06], p = 0.083).
None of the control variables age (OR = 1.05, 95% CI [1.00, 1.12], p = 0.115), gender (OR = 0.57, 95% CI [0.11, 3.07], p = 0.499), relationship status (OR = 1.47, 95% CI [0.38, 6.63], p = 0.588), or education (OR = 0.67, 95% CI [0.34, 1.29], p = 0.241) did exhibit significant associations with cluster membership.
Discussion
The current study aimed to investigate the role of resilience factors in parents of children in need of care. Consistent with previous studies (Cachia et al., 2016b; Lin et al., 2013; Peer & Hillman, 2014), our sample showed significantly impaired well-being and ability to recover from stress as well as a high mental distress compared to the general German population. The cluster analysis revealed two distinct clusters of parents: one that can be characterized as low-burden individuals and the other as high-burden individuals. This result suggests that different patterns of resilient outcomes exist among parents of children in need of care. The subsequent logistic regression analysis provided valuable insights into the significance of specific resilience factors in distinguishing between low-burden and high-burden individuals. Among the resilience factors considered, self-efficacy, social support, optimism, and family cohesion emerged as significant predictors.
As psychosocial resources, resilience factors can buffer the potentially harmful effects of stress and help to maintain or regain physical and mental health (Bonanno, 2021; Schäfer et al., 2022; Veer et al., 2021). Our findings underscore the significance of resilience factors in supporting parents facing the demands of caregiving for chronically ill and disabled children. Among these factors, social support emerges as an important resilience factor, forming an invaluable support network for parents. These social ties, comprising friends, family, and communities, offer multifaceted forms of assistance, spanning emotional, informational, and instrumental support. Furthermore, a pivotal aspect of parental resilience is the cultivation of a deep sense of control over one’s life, characterized by elevated levels of self-efficacy. When parents exhibit high self-efficacy, they tend to approach challenges associated with caregiving for children with disabilities with a profound sense of confidence. They perceive obstacles as opportunities for growth rather than insurmountable barriers. Optimism emerges as another important resilience factor. Optimism serves as a constructive coping mechanism, influencing the parents outlook on challenges and facilitating a positive and adaptive response to the demands of caregiving. Moreover, the presence of stable family ties in the form of family cohesion increases parents’ resilience and overall well-being in the caregiving role. These familial connections contribute to a cohesive support system, fostering a sense of unity, shared responsibilities, and emotional closeness. These findings are consistent with previous studies that have already demonstrated the importance of social support (Darbyshire & Stenfert Kroese, 2012; Halstead et al., 2018; Stenfert Kroese et al., 2002) and family cohesion (Boyraz & Sayger, 2011; Lightsey & Sweeney, 2008) for mental health and wellbeing among parents of children in need of care. Optimism and self-efficacy, have also been identified as important resilience factors in other studies (Baker et al., 2005; Boyraz & Sayger, 2011; Guillamón et al., 2013; Kayfitz et al., 2010; Walsh, 2003).
Practical implications
The findings of our study have implications for practice, particularly in the context of psychological interventions and support strategies for parents of children in need of care. By identifying the associations between specific resilience factors and patterns of resilient outcomes, the results contribute to the design of interventions to support parents of children in need of care. Self-efficacy, social support, optimism, and family cohesion emerged as key predictors of low-burdened clusters, which might emphasize their importance in intervention strategies. Given the significantly higher psychological distress levels observed in the parents, practical recommendations for clinicians, therapists, and family counsellors are important. Interventions should prioritize building self-efficacy, reinforcing social support, and promoting family cohesion, as these factors directly influence resilience outcomes (Darbyshire & Stenfert Kroese, 2012; Hohlfeld et al., 2018; Stenfert Kroese et al., 2002; Wilson et al., 2014). Clinicians can develop resilience-oriented therapies, while counsellors might focus on family-based strategies to enhance cohesion and reduce parental distress. Additionally, structured peer mentoring programs could provide parents with valuable role models and practical advice from those who have successfully faced similar challenges previously. These interventions should be comprehensive support programs given the complexity of resilience factors and their interconnectedness (Fritz et al., 2018). They should be designed to address the multifaceted aspects of individual and family resilience, recognizing that enhancing one aspect may positively influence others (Walsh, 2016). Addressing family dynamics and strengthening family resources like cohesion, alongside with empowering parents’ individual coping and problem-solving skills, can contribute to overall family wellbeing (Aivalioti & Pezirkianidis, 2020). Importantly, digital and telehealth interventions could offer accessible and scalable solutions, especially for families in rural or underserved areas (Schäfer et al., 2024). In terms of public policy, improving access to mental health services is crucial, particularly through increased funding and affordable options tailored to these parents. Prioritizing services such as counselling and peer support programs can reduce distress (Lancaster et al., 2023). Community-based support systems should be strengthened. Establishing local resources that promote social networks and peer support is key, as our findings and previous research shows that social support plays an important role in alleviating carers’ stress (Gise & Cohen, 2022; Wilson et al., 2014). Furthermore, public health initiatives could focus on promoting family resilience by supporting family cohesion and communication. Policies that fund family-based interventions can enhance wellbeing by improving the family’s ability to cope with caregiving demands (Gavidia‐Payne et al., 2015; Leeman et al., 2016). Finally, funding resilience-enhancing programs—such as resilience training and early intervention services—could helping parents build important coping skills (Palacio et al., 2020). Ultimately, interventions that incorporate the voices and lived experiences of parents themselves may be particularly effective, ensuring that programs align closely with their specific needs and realities. However, it is essential to interpret these findings cautiously. Given the cross-sectional nature of this study, we cannot draw strong practical implications directly from the data. Instead, our conclusions should be viewed in light of previous research. To fully understand the long-term effectiveness of individual measures, more longitudinal studies are needed.
Based on the results of our study, future research should focus on further exploring the complex and multifaceted nature of resilience in parents of children in need of care. Longitudinal studies are particularly needed to examine the dynamic nature of resilience factors and their relationship with multidimensional resilience outcomes over time (Kalisch et al., 2019; Köber et al., 2022). Such studies can shed light on the trajectories of resilience and identify critical periods where targeted interventions may be most effective. In addition to quantitative analyses as those were employed in our study, more qualitative and mixed methods studies could offer crucial insights into the protective and supportive characteristics exhibited by these parents, which may not be fully captured through quantitative measures (see for example: Janin et al., 2018; Nabors et al., 2019; Rosenberg et al., 2013). Such studies might also be used to guide the development of future quantitative measures.
Strengths and limitations
The use of cluster analysis provided a novel approach to identify distinct patterns of resilient outcomes in parents of children in need of care. Additionally, the use of logistic regression enabled us to explore the association of resilience factors with the likelihood of belonging to specific clusters, enhancing the understanding of factors associated with different levels of burden. There has been relatively little research on parents of children in need of care, therefore this study addressed an important research gap.
However, our study must be interpreted in the light of its limitations. First, our sample is not representative for the population of parents with children in need of care in Germany. The sample of the current study was recruited via specific support groups. While this approach allowed us to access a population of interest, it is essential to acknowledge the potential presence of selection bias within our sample. It is plausible that families in our sample have higher levels of awareness and access to available support services and also experience more social support (Kofahl et al., 2016; Sartore et al., 2021). Second, though our data was taken from the baseline assessment of a longitudinal study, for this analysis, we only used cross-sectional data. Thus, our analyses are based on cross-sectional associations and do not allow for causal conclusions. Third, our sample consisted almost entirely of women. While this is a common characteristic of research involving parents of children with chronic diseases or disabilities (Boyd et al., 2019b), we were unable to analyse mothers and fathers separately. Fourth, due to the length of the questionnaire, we were only able to examine a limited number of resilience factors. While the selected factors were well-established in the literature, there may be other important resilience factors not included in our analysis. Finally, no separate sample size planning was carried out for the specific analyses conducted in this paper, as the sample size planning was based on the intervention component of the NEST project.
Conclusions
In conclusion, our study conducted an exploration of resilience factors among parents of children in need of care, revealing two distinct clusters of individuals categorized as low and high burden. Notably, self-efficacy, social support, optimism, and family cohesion emerged as statistically significant predictors of membership in the low-burden clusters. These findings substantiate the critical role played by these specific resilience factors in mitigating the potential adverse consequences of stressors in parents of children in need of care.
The practical implications of our study are relevant to the design of targeted interventions and support strategies aimed at enhancing the well-being of parents engaged in caregiving for children in need of care. These interventions may adopt a combined approach that addresses both individual factors, such as self-efficacy and optimism, and familial dynamics, including family cohesion. Recognizing the intricate interplay among resilience factors, comprehensive support programs are imperative, considering the multifaceted nature of resilience dynamics.
In terms of future research, there is a clear need for more longitudinal investigations to delve into the dynamic nature of resilience factors and their evolving relationship with multidimensional resilience outcomes over time. Such longitudinal studies like the NEST-study are poised to provide invaluable insights into the trajectories of resilience among parents in caregiving roles, pinpointing crucial junctures for the implementation of targeted interventions that can most effectively bolster parental well-being in the face of caregiving challenges.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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