Although numerous articles have used the residuals approach to measuring resilience (Amstadter et al., 2014; Bowes et al., 2010; Collishaw et al., 2016; Cosco et al., 2018; de Vries et al., 2021; Ioannidis et al., 2020; Miller-Lewis et al., 2013; Sapouna & Wolke, 2013; van Harmelen et al., 2017), to our knowledge, no published studies have formally investigated the validity of this approach. Our study has found that the residuals approach to measuring resilience has both construct and predictive validity. Seven resilience factors were associated with higher levels of resilience and their effect on resilience was in line with predictions based on prior research. Resilience significantly predicted a reduction in the risk of having depressive symptoms at 18 years old and predicted a reduction in the likelihood of having NEET status at 17 and 23 years. Surprisingly, no socioeconomic factors were found to be associated with resilience.
Individual Resilience Factors
We sought to assess the validity of this methodology by investigating whether previously identified resilience factors and demographic factors significantly predict resilience when measured by the residuals approach. To demonstrate the validity of our approach, we investigated whether individual factors previously associated with an increase in resilience significantly predicted resilience as measured by the residual approach at 16 years in our ALSPAC sample. Indeed, we found high cognitive skills (at 6 years 9 months), reading comprehension (at 9 years), high global self-worth (8 years), and a less emotional temperament (5 years 9 months) represent intrinsic individual level RFs that continue to exert their positive effects for some length of time. They could also be described as generative, setting positive cascades in place that develop other contributing factors such as coping styles and emotion regulation (Luthar et al., 2006). A less emotional temperament in childhood, described as biologically-based individual differences in reactivity and regulation (Rothbart, 2006), was a significant predictor of resilience at 16 years. These findings are consistent with previous research that has found children with less emotional temperaments are less reactive to stressors, better able to regulate their feelings of sadness and anger, more likely to maintain positive adaptation and activate flexible coping strategies to deal with adversity (Compas et al., 2004; Martinez-Torteya et al., 2009; Olson et al., 2002). The finding that temperament in childhood predicts resilience in adolescence therefore supports the construct validity of the residual measurement of resilience.
High cognitive skills have previously been associated with positive adaptation in the face of adversity (Gartland et al., 2019; Jaffee et al., 2007), predictive of lower levels of psychiatric disorders, lower rates of conduct problems and higher levels of overall functioning (Malcarne et al., 2000). While having a high IQ was just below the significance threshold in our model predicting resilience, high cognitive skills and high reading comprehension significantly predicted resilience. Having well-developed verbal cognitive abilities could allow children to use verbal strategies to mediate conflict, leading to more circumstance-appropriate behavioural choices and a larger range of coping strategies (Buckner et al., 2003). In addition to cognitive skills, high global self-worth at 8 years was associated with higher levels of resilience at 16 years. Self-worth is an intrapersonal characteristic that has been previously reported to impact an individual’s potential for resilience (Davey et al., 2003; Reyes, 2008). Individuals with high self-worth have high amounts of self-respect, and have positive feelings about themselves, their environment and their ability to deal with life’s challenges, focussing on their strengths (Rutter, 1989; Werner, 1994). Higher self-worth has been linked to positive cognitive reappraisal (Schwerdtfeger et al., 2019), an underlying mechanism that protects against stressors and mediates RFs via cognitive processes (Kalisch et al., 2015).
Family Resilience Factors
In terms of family resilience factors, previous studies have had broad support for high quality caregiver relationships and stable family environments (Afifi & MacMillan, 2011; Gartland et al., 2019; Haskett et al., 2006). We found an association between positive sibling relationship and resilience. High-quality sibling relationships are a unique context which can have a direct impact on one another’s socioemotional development, behaviour and adjustment, relevant to resilience (Dirks et al., 2015; McHale et al., 2012). It is interesting to note that women who have had one or three previous children, and two just below the threshold, was also significantly associated with an increase in resilience in their offspring, which may suggest that having siblings increases resilience in adolescence. This enduring association between multiparous (giving birth previously) and resilience could also be related to the extensive physiological, hormonal, and emotional changes experienced by the mother during pregnancy and the postpartum period. The hormonal fluctuations required for the maintenance of pregnancy are unmatched by any other neuroendocrine events in a healthy female’s lifetime (Brunton & Russell, 2010), with dramatic changes also evident in metabolic, immune, cardiovascular, pulmonary, haematological and neurobiological systems (Deems & Leuner, 2020). Despite parity being commonly included as a covariate in studies of psychological outcomes of children of multiparous women, the origins of parity-associated differences in these outcomes remain poorly defined. It is possible that the observed differences reflecting effects of prior pregnancy on adaptation to subsequent pregnancies/children. However, well-documented increased activation of the Hypothalamic-pituitary-adrenal (HPA) axis (e.g.(Conde & Figueiredo, 2014)) and sympathetic nervous system (DiPietro et al., 2005) in pregnancy is consistent with well-described physiological responses to psychological stress, supporting the role of psychological factors (Gillespie et al., 2018). Maternal mental health problems are also less common following a multiparous pregnancy (e.g. (Iwata et al., 2016)). Specifically related to resilience, parity has been associated with a modification in disease risk and progression of multiple sclerosis, depression, stroke and Alzheimer’s disease in the mother (Deems & Leuner, 2020). Disentangling the mechanisms by which maternal parity is associated with adolescent resilience warrants further investigations.
We found little evidence in support of the relationship between grandparent attachment and resilience, perhaps due to the maternal self-reports used in ALSPAC. Although there is some evidence of detection bias, there is evidence of intergenerational transmission of child abuse (Widom et al., 2015). An abusive parent may have been subjected to abuse by their own parent, hence the limited evidence for grandparents exerting a positive influence and predicting resilience. Our construct of the maternal parenting score within ALSPAC had such high missingness (>80%) that it had to be removed from the missing data analysis. Future research would benefit from including some measure of positive parental engagement as a protective factor.
Community Resilience Factors
Within our framework of community level resilience factors, factors relating to school, including positive opinion of school and regular participation in extracurricular activities were associated with higher levels of resilience. These extrinsic school-based factors are in keeping with the dynamic model of resilience, which conceptualizes resilience not as an individual trait but a process resulting from interactions across the life span, dependent upon context and resources (Cicchetti & Blender, 2006; Kalisch et al., 2017; Rutter, 2012). Ungar (Ungar, 2015) proposes that when stressors are particularly high, environmental factors become more critical for a person’s resilience than individual characteristics or cognitions.
Lack of Association between Socioeconomic Factors and Resilience
None of the socioeconomic factors were significantly associated with resilience. This is a somewhat surprising result given that health is well established to be socioeconomically stratified, with those in socioeconomically disadvantaged groups at a greater risk of negative physical and psychological outcomes when compared with socioeconomically advantaged groups (Marmot, 2005). Adult socioeconomic advantage has been previously associated with adult resilience, measured at 60 – 64 years using the residuals methodology (Cosco et al., 2018), yet there is scant evidence of childhood socioeconomic advantage being associated with adolescent resilience. If socioeconomic disadvantage is a major determinant of health, then emotional and cognitive responses to this inequality are of crucial importance and it is likely that the residuals approach is capturing these responses, rather than capturing socioeconomic dis/advantage itself. Future research using the same methodological techniques for measuring resilience could assess whether child socioeconomic disadvantage followed by adult socioeconomic advantage (i.e., upward intergenerational social mobility) is associated with greater resilience. This would support the theory of ‘steeling’ i.e., developing resilience through exposure to mild aversity in early life, which is supported by positive results in animal models (e.g. (Lyons et al., 2009)) yet has limited results in humans (Rutter, 2012).
Implications for Interventions
Our study provides support for construct validity of the residuals approach to measuring resilience and suggests some key areas that have important implications for policy, practice, and future work. We note that while the results of this study may be informative for population-level or structural policies, we are not individualising the problem or suggesting to place the onus on individuals to act. All of our recommendations for targeted levels of intervention are within the structural social context in which the children are exposed to ACEs (see (Kelly-Irving & Delpierre, 2019), for further discussion). While many of the individual RFs we measured are not easily modifiable, there is scope for intervention researchers to have success in enhancing language development (Goldin-Meadow et al., 2014), cognitive and social-emotional development (Schonert-Reichl et al., 2015) and global self-worth through physical activity (Haugen et al., 2011). Intervening in sibling interactions may be useful to encourage high-quality sibling relationships, with two prevention programs already in place in the US (More Fun with Sisters and Brothers (Kennedy & Kramer, 2008) and Siblings are Special (Feinberg et al., 2013)). At the community level, our study suggests the school environment is the most important area for policy to focus on and, given that the key protective factors in our study were identified between 5 and 14 years, individuals may particularly benefit from interventions in primary school, particularly school-based strategies that offer a range of extracurricular activities and enable children to feel more positive about school. Policy could target areas that encapsulate multiple protective factors and their intertwined relationships together. For example, the link between physical activity, global self-worth and adaptive cognitive reappraisal (Haugen et al., 2011; Perchtold-Stefan et al., 2020) could benefit from extracurricular sports programs.
Predictive Validity – Depressive Symptoms
We investigated the predictive validity of resilience by comparing the predictive power of the residuals method of quantifying resilience with other determinants of psychosocial functioning on two outcomes. Adjusting for ACEs and other sociodemographic factors associated with increased depressive symptoms, resilience was significantly associated with reduced depressive symptoms at age 18 [the first outcome in the predictive validity analyses, (std. β = -0.12, p<0.001). These results strongly support the predictive validity of the residuals method of measuring resilience. The identification of resilience as a specific protective factor associated with lower reports of depressive symptoms can inform the development of prevention and treatment interventions for depression. Specifically, the strategies mentioned above promoting resilience in all children, not just those exposed to ACEs, would be beneficial. Additionally, measuring an adolescent’s resilience to SDQ at 16 may be highly informative. Individual differences in resilience scores may have consequences for tailoring prevention interventions for psychiatric disorders. Further research is needed to explore to what extent this measure of resilience has predictive value for prevention and/or clinical intervention.
Predictive Validity – NEET status
In the second predictive validity analysis, resilience also predicted a reduced likelihood of NEET status at both 17 years (OR = 0.93, 95% CI = 0.89 – 0.97) and 23 years (OR = 0.92, 95% CI = 0.89 – 0.95). While these are modest effect sizes, they are in line with effect sizes previously associated with the predictive validity of self-assessed resilience (Campbell‐Sills et al., 2018) and the social competence resilience factor of the resilience scale for adolescents (Hjemdal et al., 2007). The continued stable effect of resilience on reduced likelihood of NEET status from 17 to 23 shows that the predictive validity of resilience is enduring. These results indicate that resilience has value as a predictor of both depressive symptoms and risk of NEET status.
Research and Clinical Implications
There are research and clinical implications that can be derived from this study. First, the residuals approach to measuring resilience has both construct and predictive validity: it is a measure of the current resilience of an individual, where resilient functioning refers to better mental wellbeing compared to other individuals with similar ACE exposures. Accordingly, resilience researchers can benefit from this measurement of resilience to determine specific resilience factors at the individual, family and community level that are associated with higher levels of resilience. In addition, this measure of resilience can be used as an independent variable to predict various outcome variables such as psychosocial outcomes and overall functioning.
The greatest strength of this measurement of resilience is its ability to be derived from a simpler computational framework that does not require specialised latent variable modelling software, which therefore supports the widespread application of this method. Because this measure is data-driven, it is a measure of the current resilience of an individual, where resilient functioning refers to better mental wellbeing compared to other individuals with similar ACE exposures. The derived measure is influenced by the specific variables used and provides an individual operationalisation of resilience that is relatively simple to compute.
The novelty of this method of quantifying resilience is not that it demonstrates the protective effects of resilience factors. Extensive previous research on resilience factors have shown the positive influence of resilience on important outcomes (Johnson & Wiechelt, 2004; Khambati et al., 2018). Instead, its originality and significance lie in its ability to advance two key research areas that cannot be adequately studied using other measures: 1) mechanisms of resilience and 2) efficacy of interventions designed to increase resilience.
First, the mechanisms underlying the protective effects of resilience are best examined with a quantitative, individual-specific variable that represents the sum of the resilience construct. Proxy measures may represent one small aspect of an individual’s total resilience, which includes a vast array of life experiences or adversity exposure but are difficult to measure. Extracting a quantitative measure of resilience that is not rooted in any one definition or measured by one static tool, is a step towards identifying underlying general resilience mechanisms (Ioannidis et al., 2020; Kalisch et al., 2015).
Second, this quantitative measure of resilience can be measured longitudinally and used as an ongoing measure of change through therapeutic processes. The ability to assess an individual’s resilience at the outset of intervention provides a beneficial starting point for strength-based, individual focused care. Extracting a measure of resilience that is sensitive to change can better inform these potential interventions. Similarly, by quantifying resilience at multiple time points, one can characterise individual differences in the variation of resilience and ascertain the impact of resilience factors at the individual, family and community level at varying timepoints across the life course. Future studies are needed to explore this.
There are some limitations in the present study that must be acknowledged. First, as with most longitudinal cohorts, there was attrition in all outcomes. Whilst we attempted to minimize the impact of this using multiple imputation with chained equations, this approach cannot remove bias completely. Secondly, our dataset of SDQ outcomes was derived from maternal reports but the parents may underestimate conduct problems. However, mean ALSPAC scores are similar to national levels (Meltzer et al., 2003). Thirdly, the results found here may be unique to the ALSPAC cohort, a cohort that is very white, with a higher proportion of married mothers who own their own home than the rest of the general population. We therefore need to expand and diversify the sample to allow for these results to be translatable at the population level. To increase the reliability of this measure of resilience, we propose a replication study in a different dataset. Finally, the correlational design cannot determine causal relations, and prospective or experimental studies are needed.