Introduction
Childhood adversity is a common risk factor associated with adolescent mental health difficulties, with one in two individuals experiencing abuse, neglect, our household dysfunction in the UK (Bellis et al., Reference Bellis, Hughes, Leckenby, Perkins and Lowey2014). There is increasing interest in identifying childhood experiences that may be protective against mental health difficulties following childhood adversity. School connectedness has been suggested as a potential target for interventions to promote adolescent mental health and protect against childhood adversity. However, the potential protective effects of school connectedness against childhood adversity remain unclear, specifically if feeling connected to school at certain ages is critical, how long protective effects last, and if the relationship only pertains to specific mental health outcomes (i.e. externalizing problems, internalizing problems, or positive mental health). To further understand the protective effects of school connectedness and inform future interventions, the current study explored the longitudinal relationships among childhood adversity, school connectedness, and adolescent mental health outcomes through a secondary data analysis of the United Kingdom Millennium Cohort Study.
Adverse childhood experiences
Adverse childhood experiences (ACEs) are events in childhood that are strongly associated with poor adolescent mental health, including physical, emotional, and sexual abuse; physical and emotional neglect; parental mental illness, domestic violence, divorce, having an incarcerated relative, and parental substance abuse. Felitti et al. (Reference Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards and Marks1998) first discovered a dose-response relationship between ACEs and health risk behavior and disease among over 13,000 adults, which has been replicated among more diverse samples (Burke et al., Reference Burke, Hellman, Scott, Weems and Carrion2011; McLaughlin et al., Reference McLaughlin, Green, Gruber, Sampson, Zaslavsky and Kessler2012). The number of ACEs one has experienced is often calculated as an individual’s ACE score, representing their cumulative exposure to childhood adversity. While the cumulative risk approach fails to recognize the differential effects of specific forms of adversity, it is helpful in identifying children in most need of interventions (McLaughlin & Sheridan, Reference McLaughlin and Sheridan2016). ACE scores shed important light on the profound impact early adversity has on subsequent mental health and are standardly deployed as measures of childhood adversity in research (Hamby et al., Reference Hamby, Elm, Howell and Merrick2021; Hughes et al., Reference Hughes, Bellis, Hardcastle, Sethi, Butchart, Mikton, Jones and Dunne2017; Lacey & Minnis, Reference Lacey and Minnis2020).
Mental health
Mental health is multidimensional, characterized by the lack of mental illness and presence of positive affect i.e., pleasurable experiences and positive moods such as joy and interest (Miller, Reference Miller, Goldstein and Naglieri2011). The World Health Organization (2022) recognizes this dual-factor model of mental health, stating that mental health enables individuals to cope with life stressors and function well in society. In the context of this study, mental health is more than the absence of mental illness and includes positive functioning and well-being; specifically, three constructs comprise mental health: externalizing problems, internalizing problems, and positive mental health.
Mental health difficulties often are conceptualized as externalizing or internalizing problems (Achenbach, Reference Achenbach1978). Externalizing problems are negative behaviors acted out on an individual’s environment (Campbell et al., Reference Campbell, Shaw and Gilliom2000). In children and adolescents, these manifest as aggression, delinquency, or hyperactivity (Liu, Reference Liu2004), and predict adult crime, violence, and substance use (Brook et al., Reference Brook, Brook, Rubenstone, Zhang and Saar2011; Gornik et al., Reference Gornik, Clark, Durbin and Zucker2023; Miettunen et al., Reference Miettunen, Murray, Jones, Mäki, Ebeling, Taanila, Joukamaa, Savolainen, Törmänen, Järvelin, Veijola and Moilanen2014). Internalizing problems reflect individuals’ emotional and psychological states, including depressive symptoms, anxiety, and suicidal ideation (Liu et al., Reference Liu, Chen and Lewis2011). Internalizing problems are associated with negative consequences such as academic struggles, school dropout, suicide, and juvenile delinquency (Liu et al., Reference Liu, Chen and Lewis2011). Such externalizing and internalizing problems often co-occur within children (Bird et al., Reference Bird, Gould and Staghezza1993; Caron & Rutter, Reference Caron and Rutter1991; Copeland et al., Reference Copeland, Adair, Smetanin, Stiff, Briante, Colman, Fergusson, Horwood, Poulton, Jane Costello and Angold2013; Willner et al., Reference Willner, Gatzke-Kopp and Bray2016).
Positive mental health consists of affective and psychological components. The affective component reflects an individual’s subjective feelings of satisfaction with life and themselves, while the psychological element focuses on cognitive and emotional functioning (Clarke et al., Reference Clarke, Friede, Putz, Ashdown, Martin, Blake and Stewart-Brown2011; Tennant et al., Reference Tennant, Hiller, Fishwick, Platt, Joseph, Weich, Parkinson, Secker and Stewart-Brown2007). Put simply, positive mental health entails feeling good and functioning well and is often used interchangeably with mental well-being. In this study, “mental health” collectively refers to externalizing problems, internalizing problems, and positive mental health.
Mental health difficulties are common among adolescents in the UK. In 2022, 18% of children between the ages of 7–16 had a probable mental disorder (Newlove-Delgado et al., Reference Newlove-Delgado, Marcheselli, Williams, Mandalia, Davis, McManus, Savic, Treloar and Ford2022). Adolescence is the peak age of onset for most mental health problems, suggesting interventions at or before this period are necessary to prevent mental health difficulties (Paus et al., Reference Paus, Keshavan and Giedd2008). The brain is most plastic during childhood and adolescence, further highlighting the importance of early interventions to mitigate risk factors and promote protective experiences (Lee et al., Reference Lee, Heimer, Giedd, Lein, Sestan, Weinberger and Casey2014; Romeo, Reference Romeo2013).
Adverse childhood experiences and mental health
ACEs are associated with an increased risk of mental health problems across the life course. On a social level, ACEs are associated with loneliness, social isolation, and feeling less close to others in adulthood (Hanlon et al., Reference Hanlon, McCallum, Jani, McQueenie, Lee and Mair2020; Hughes et al., Reference Hughes, Lowey, Quigg and Bellis2016; Nurius et al., Reference Nurius, Green, Logan-Greene and Borja2015). Notably, these social factors coincide with psychiatric risk. Childhood adversity is associated with an increased risk of depression and anxiety (Gomis-Pomares &Villanueva, Reference Gomis-Pomares and Villanueva2022; Li et al., Reference Li, D’Arcy and Meng2016; Yap et al., Reference Yap, Pilkington, Ryan and Jorm2014), substance use disorders (Syer et al., Reference Syer, Clarke, Healy, O’Donnell, Cole, Cannon and McKay2021), and mental health diagnoses (McKay et al., Reference McKay, Kilmartin, Meagher, Cannon, Healy and Clarke2022). Nearly 50% of mental health problems emerge before the age of 14 (Kessler et al., Reference Kessler, Berglund, Demler, Jin, Merikangas and Walters2005), suggesting the risk associated with ACEs begins in childhood or adolescence. Indeed, childhood adversities are associated with an increased risk of mental health difficulties (Green et al., Reference Green, McLaughlin, Berglund, Gruber, Sampson, Zaslavsky and Kessler2010).
Beyond predicting adult mental health difficulties, ACEs are associated with negative mental health in youth, ranging from psychiatric diagnoses to behavior problems (Scully et al., Reference Scully, McLaughlin and Fitzgerald2020). Specifically, childhood adversity is associated with greater externalizing and internalizing problems in adolescence (Balistreri & Alvira-Hammond, Reference Balistreri and Alvira-Hammond2016; Healy et al., Reference Healy, Eaton, Cotter, Carter, Dhondt and Cannon2022; James et al., Reference James, Jimenez, Wade and Nepomnyaschy2021; Schilling et al., Reference Schilling, Aseltine and Gore2007; Van Loon et al., Reference Van Loon, Van De Ven, Van Doesum, Hosman and Witteman2015; Wan & Leung, Reference Wan and Leung2010). Longitudinal studies demonstrate that childhood and adolescent mental health problems associated with ACEs arise in early childhood (Bevilacqua et al., Reference Bevilacqua, Kelly, Heilmann, Priest and Lacey2021, Choi et al., Reference Choi, Wang and Jackson2019, Green et al., Reference Green, McLaughlin, Berglund, Gruber, Sampson, Zaslavsky and Kessler2010; Stein et al., Reference Stein, Sheridan, Copeland, Machlin, Carpenter and Egger2022). Research on the developmental timing of childhood adversity has yielded mixed results (Schaefer et al., Reference Schaefer, Cheng and Dunn2022), but prospective studies have found that adversity before the age of 5 years is more strongly associated with risk of mental health problems (Duprey et al., Reference Duprey, Oshri and Caughy2017; Kaplow & Widom, Reference Kaplow and Widom2007; Keiley et al., Reference Keiley, Howe, Dodge, Bates and Pettit2001). The brain undergoes rapid structural changes associated with higher cognitive function during the first years of life, leading to heightened sensitivity to stressors, which could explain the greater risk of psychopathology associated with adversity before the age of 5 (Nelson, Reference Nelson2000). Additionally, researchers have hypothesized that if early developmental disturbances are not resolved, there is increased risk of failure to achieve subsequent developmental milestones, which may ultimately lead to psychopathology (Aber et al., Reference Aber, Allen, Carlson and Cicchetti1989; Cicchetti, Reference Cicchetti1989). While the relationship between early childhood adversity and mental health is clearly established, not all children who experience early adversity develop mental health difficulties, sparking exploration into protective factors.
Resilience and childhood adversity
Decades of developmental psychology research have consistently identified childhood experiences that independently promote positive mental health and counteract the risk of childhood adversity (Masten, Reference Masten2001, Reference Masten2007, Reference Masten2014). The Resiliency Theory framework (Masten & Cicchetti, Reference Masten and Cicchetti2016) contextualizes how positive childhood experiences may be protective against childhood adversity. Resilience is the ability of children to successfully adapt to stressors that threaten positive development (Masten & Cicchetti, Reference Masten and Cicchetti2016). From a developmental systems perspective, children grow up in complex, interconnected, and interactive systems, ranging from proximal influences including families and schools, and more distal systems such as culture and governments. These overlapping contexts shape children’s development and are foundational to one’s mental health and resilience (Hyde et al., Reference Hyde, Gard, Tomlinson, Burt, Mitchell and Monk2020).
The Compensatory and Protective Factors Models of Resiliency Theory classify factors associated with resilience. The Compensatory Model states that promotive factors have a direct and independent effect on the outcome of interest, in the opposite direction of risk factors (Zimmerman et al., Reference Zimmerman, Stoddard, Eisman, Caldwell, Aiyer and Miller2013). Furthermore, these promotive factors may mitigate the negative effects of risk factors. Additionally, the Protective Factors Model of Resiliency postulates that positive experiences moderate the relationship between risk factors and the outcome (Zimmerman et al., Reference Zimmerman, Stoddard, Eisman, Caldwell, Aiyer and Miller2013). In the context of ACEs, protective factors are those that weaken the relationship between ACEs and mental health difficulties.
Protective factors may function by disrupting negative developmental cascades. The developmental cascade framework suggests that events and characteristics of early development can “snowball” or cause cumulative consequences in greater magnitude or different domains later in development (Masten & Cicchetti, Reference Masten and Cicchetti2010). Due to varying contexts among individuals, generalizing protective effects is a difficult task; however, emotional security and strong relationships consistently facilitate adaptation among at-risk individuals, highlighting the role of social support in resilience (Bethell et al., Reference Bethell, Jones, Gombojav, Linkenbach and Sege2019; Brinker & Cheruvu, Reference Brinker and Cheruvu2016; Rutter, Reference Rutter1987; Schofield et al., Reference Schofield, Lee and Merrick2013; van Harmelen et al, Reference van Harmelen, Gibson, St Clair, Owens, Brodbeck, Dunn, Lewis, Croudace, Jones, Kievit, Goodyer and Alway2016). Positive parent-child relationships have been found to buffer the risk of internalizing and externalizing problems associated with adverse life events in the MCS dataset (Flouri et al., Reference Flouri, Midouhas, Joshi and Tzavidis2015), which raises the question if support outside the home could lead to similar protective effects.
The neurocognitive social transactional model is an example of a negative developmental cascade in which school connectedness could function as a protective factor (McCrory et al., Reference McCrory, Foulkes and Viding2022). The model posits that childhood adversity causes neurobiological changes including altered threat processing, atypical processing of social reward, and difficulties with autobiographical memory (McCrory et al., Reference McCrory, Foulkes and Viding2022; Teicher et al., Reference Teicher, Samson, Anderson and Ohashi2016). These altered behaviors can lead to “social thinning,” the decrease in quality and number of supportive relationships, and stress generation, an increased likelihood to experience stressors (McCrory et al., Reference McCrory, Foulkes and Viding2022). This lack of social support and increased stress can cause and exacerbate mental health problems that are associated with childhood adversity. Empirical evidence supports this theory, as individuals with a history of ACEs typically have impoverished social networks with fewer friends of the same age and classmates (Negriff et al., Reference Negriff, James and Trickett2015; Nevard et al., Reference Nevard, Green, Bell, Gellatly, Brooks and Bee2021; Salzinger et al., Reference Salzinger, Feldman, Hammer and Rosario1993) and are more likely to experience stressful life events (Gerin et al., Reference Gerin, Viding, Pingault, Puetz, Knodt, Radtke, Brigidi, Swartz, Hariri and McCrory2019; Harkness et al., Reference Harkness, Lumley and Truss2008; Uhrlass & Gibb, Reference Uhrlass and Gibb2007). In turn, low levels of social support have been found to predict mental health difficulties (Brugha et al., Reference Brugha, Weich, Singleton, Lewis, Bebbington, Jenkins and Meltzer2005; Matthews et al., Reference Matthews, Danese, Caspi, Fisher, Goldman-Mellor, Kepa, Moffitt, Odgers and Arseneault2019; McLafferty et al., Reference McLafferty, O’Neill, Armour, Murphy and Bunting2018; Nevard et al., Reference Nevard, Green, Bell, Gellatly, Brooks and Bee2021), and chronic stress is a well-regarded risk factor for mental health (Grant et al., Reference Grant, Compas, Thurm, McMahon and Gipson2004; Turner & Lloyd, Reference Turner and Lloyd2004). Studies have also identified that childhood adversity affects subsequent mental health problems through its intermediary influence on social support (Hyman et al., Reference Hyman, Gold and Cott2003; Lagdon et al., Reference Lagdon, Ross, Robinson, Contractor, Charak and Armour2021; McLafferty et al., Reference McLafferty, O’Neill, Armour, Murphy and Bunting2018; Owen et al., Reference Owen, Thompson, Mitchell, Kennebrew, Paranjape, Reddick, Hargrove and Kaslow2008; Powers et al., Reference Powers, Ressler and Bradley2009; Salazar et al., Reference Salazar, Keller and Courtney2011; Sheikh et al., Reference Sheikh, Abelsen and Olsen2016; Sperry & Widom, Reference Sperry and Widom2013; Stevens et al., Reference Stevens, Gerhart, Goldsmith, Heath, Chesney and Hobfoll2013; Vranceanu et al., Reference Vranceanu, Hobfoll and Johnson2007). Resilience interventions often target promotive and protective processes to disrupt negative developmental cascades (Cicchetti & Hinshaw, Reference Cicchetti and Hinshaw2002; Masten & Cicchetti, Reference Masten and Cicchetti2016), and under the neurocognitive social transactional model, increased social support and stress-buffering systems could counteract social thinning and stress generation and explain how strong relationships mitigate the negative mental health effects associated with childhood adversity.
Schools are a prime setting for interventions aimed to protect against childhood adversity. Often distal influences affect children more directly through proximal systems (Bronfenbrenner, Reference Bronfenbrenner1977), drawing attention to how systems youth interact with frequently can change for their benefit. Outside of the family, schools are the most organized system in which children spend most of their time and are home to successful interventions aimed at nurturing childhood resilience (Eccles and Roeser, Reference Eccles, Roeser, Mayes and Lewis2012; Masten, Reference Masten2014). Furthermore, individuals begin to rely more on their peers relative to family as they enter adolescence, suggesting the school setting may be particularly useful in bolstering adolescents’ social support and relationships (Goodenow, Reference Goodenow1993; Nelson et al., Reference Nelson, Jarcho and Guyer2016). Due to the strong ecological force of education systems in adolescence and the potential role of social support via classmates and teachers, school connectedness – feelings of belonging and social support at school – is a potentially promotive and protective factor against childhood adversity (Libbey, Reference Libbey2007; McNeely et al., Reference McNeely, Nonnemaker and Blum2002).
School connectedness
School connectedness benefits adolescent mental health and may mitigate the consequences of childhood adversity. The construct captures how positively students think, feel, and engage with the school environment and those within it (Hodges et al., Reference Hodges, Cordier, Joosten, Bourke-Taylor and Speyer2018; Libbey, Reference Libbey2004). This definition spans three domains: (1) cognition: the perception of support and peer and teacher relationships (2) affect: ranging from acceptance and respect to valuing and enjoying school, and (3) behavior: active engagement in school, both academically and socially. School connectedness is of particular interest to interventionists due to its modifiable nature and ability to promote mental well-being.
School connectedness is a malleable factor that schools can cultivate. McNeely et al. (Reference McNeely, Nonnemaker and Blum2002) found that positive classroom climates, extracurricular participation, tolerant disciplinary procedures, and small school size promote school connectedness. Whole school approaches designed to increase students’ feelings of school connectedness are also effective (Chapman et al., Reference Chapman, Buckley, Sheehan and Shochet2013), in addition to students feeling like their school counselor effectively responds to their problems (Martin & Sorensen, Reference Martin and Sorensen2020), the integration of relevant content into classrooms (Kim & Cappella, Reference Kim and Cappella2016), and classroom management strategies that cultivate autonomy, care, and connection (Acosta et al., Reference Acosta, Chinman, Ebener, Malone, Phillips and Wilks2019; Kiefer & Pennington, Reference Kiefer and Pennington2016). The modifiable nature of school connectedness suggests it may be an opportunity to improve young people’s mental health.
School connectedness as a promotive and protective factor
Given school connectedness’s relation to social support and stress-buffering capacity, it is a potentially protective factor against childhood adversity (Libbey, Reference Libbey2007; McNeely et al., Reference McNeely, Nonnemaker and Blum2002). In fact, school connectedness may promote mental health and well-being and mitigates the risk associated with childhood adversity.
School connectedness is a promotive factor for externalizing behaviors and has been found to be protective against childhood adversity. Studies have identified school connectedness as a strong predictor for externalizing behaviors, including substance use, violence, and risky behavior (Blum et al., Reference Blum, McNeely and Rinehart2002; Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021; Hardaway et al., Reference Hardaway, McLoyd and Wood2012; Resnick et al., Reference Resnick, Harris and Blum1993, Reference Resnick, Bearman, Blum, Bauman, Harris, Jones and Udry1997). Furthermore, school connectedness has found to be protective against the ACEs of negative family relations, peer victimization, violence exposure, and social deprivation (Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021; Hardaway et al., Reference Hardaway, McLoyd and Wood2012; Loukas et al., Reference Loukas, Roalson and Herrera2010; Loukas & Pasch, Reference Loukas and Pasch2013). The promotive and protective effects of school connectedness also extend to internalizing problems.
School connectedness is associated with fewer depressive and anxiety symptoms in adolescents, even among individuals who have experienced childhood adversity (Huang & Baxter, Reference Huang and Baxter2021; Lester et al., Reference Lester, Waters and Cross2013; Markowitz, Reference Markowitz2017; Raniti et al., Reference Raniti, Rakesh, Patton and Sawyer2022; Schwerdtfeger et al., Reference Schwerdtfeger Gallus, Shreffler, Merten and Cox2015). Feeling connected to school has also been found to be protective against childhood adversity and cyberbullying when predicting suicidal ideation and attempts (Kim et al., Reference Kim, Walsh, Pike and Thompson2020; Lensch et al., Reference Lensch, Clements-Nolle, Oman, Evans, Lu and Yang2021). Adding to this, some researchers have reported that school connectedness mediates the relationship between stressful life events and depression in adolescence (Huang & Baxter, Reference Huang and Baxter2021). Despite this, the moderator role of school connectedness in the potential direct associations between ACEs and mental health difficulties have remained questionable as findings from previous studies are not consistent (e.g., Schwerdtfeger et al., Reference Schwerdtfeger Gallus, Shreffler, Merten and Cox2015; Shochet et al., Reference Shochet, Dadds, Ham and Montague2006). While researchers have replicated the promotional effects of school, the protective effects of school connectedness for internalizing symptoms are less clear, meaning it is unknown whether or not school connectedness weakens the direct relationship between childhood adversity and adolescent mental health difficulties.
Fewer studies have examined promotional and protective effects of school connectedness on positive mental health. School connectedness is positively correlated with emotional well-being among secondary school students, a promotional effect (Arif et al., Reference Arif, Khan, Rauf and Sadia2019; Frydenberg et al., Reference Frydenberg, Care, Chan and Freeman2009). Goetschius et al. (Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021) found that school connectedness at age nine significantly moderates the relationship between childhood social deprivation and positive functioning at age 15, demonstrating the potential protective benefits of school connectedness. However, they did not find the same protective effect in the interaction between violence exposure and school connectedness, suggesting school connectedness may interact differently with specific forms of adversity.
The present study
The present study aims to strengthen previous research through exploring how school connectedness at ages 11 and 14 moderates the relationship between childhood adversity before age five and adolescent mental health outcomes (externalizing and internalizing problems at ages 14 and 17 and positive mental health at age 17). Modeling these variables simultaneously via structural equation modeling provides a more detailed picture of the relationships between predictors, moderators, and outcomes. This study will extend the work by Goetschius et al. (Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021) by examining mental health outcomes at age 17, allowing the investigation of more distal effects of school connectedness.
We were interested in addressing several research questions. First, what is the relationship between childhood adversity before age five and mental health outcomes at ages 14 and 17? (Research Question 1). We expected childhood adversity to predict higher levels of externalizing and internalizing problems and lower levels of positive mental health. Second, what is the relationship between school connectedness and mental health outcomes at ages 14 and 17? (Research Question 2a) and how does the timing of school connectedness (age 11 vs. age 14) affect this relationship? (Research Question 2b). We expected school connectedness to predict lower levels of externalizing and internalizing problems and higher levels of positive mental health. Additionally, we expected that age 14 school connectedness will exhibit greater promotive effects on mental health outcomes relative to age 11 school connectedness due to closer proximity to the measured outcomes. Finally, does school connectedness serve as a protective factor between early life adversity and mental health outcomes at ages 14 and 17 (Research Question 3a) and how does the timing of school connectedness (age 11 vs age 14) affect this relationship? (Research Question 3b). We made no predictions for these questions given the inconsistency in the literature (RQ 3 & RQ 3a).
Method
Sample
The study was a secondary analysis of existing data from the Millennium Cohort Study (MCS). The MCS is a longitudinal cohort study that has followed approximately 19,000 individuals born in the United Kingdom (UK) between 2000 and 2002 (Connelly & Platt, Reference Connelly and Platt2014). MCS families were first interviewed when the cohort member was nine months old, with follow-up data collection at ages three, five, seven, 11, 14, and 17 years. The MCS intentionally oversampled families living in poverty and ethnic minorities through stratified cluster sampling (Plewis et al., Reference Plewis, Calderwood, Hawkes, Hughes and Joshi2007). The data includes information about participants’ physical and mental health, relationships and family, school, and demographic backgrounds. Data collection methods include cognitive assessments, physical measurements, parent and cohort member interviews, and questionnaires. The National Health Service Research Ethics Committee system approved all waves of data collection for the MCS (Shepherd & Gilbert, Reference Shepherd and Gilbert2019). Ethical approval for secondary analysis of the data was sought from the Department of Education Ethics Committee at the University of York.
Analyses for the present study used data from the age three, five, seven, 11, 14, and 17 data sweeps. Only the oldest children in each family were included in the analysis, and each data sweep was merged to produce a maximum sample of 17,343 individuals. The main predictor variable (childhood adversity) was assessed at ages three and five, moderators (school connectedness) at ages 11 and 14, and mental health outcomes at ages 14 and 17. Data at ages three and five were collected via interviews with cohort members’ parents. The main interview was most often completed by the mother, and the partner interview was completed by the father. The young people completed questionnaires regarding school at the ages of 11 and 14. The self-complete questionnaire at age 11 was only administered to young people in England and Wales, restricting the age 11 sample to young people living in these countries. A majority of cohort members in the age 11 data sweep were in primary school at the time of the survey (96%), while the remaining cohort members were already in secondary school. Mental health data at age 14 were collected by parent report, and at age 17, cohort members completed mental health questionnaires.
Individuals were excluded from the analyses if all outcome data at the age of 17 were missing (n = 7,347) or if each of the variables from the data sweeps at ages three, five, 11, and 14 were missing (n = 69), resulting in a final sample size of 9,964 young people.
Measures
Adverse childhood experiences (ACEs)
Based on the original ACEs study by Felitti et al. (Reference Felitti, Anda, Nordenberg, Williamson, Spitz, Edwards and Marks1998), seven ACEs were included in ACE scores for individuals: parental divorce, parental mental illness, parental alcohol consumption, domestic violence, parental drug use, physical punishment, and verbal maltreatment. These experiences are commonly used in other studies, facilitating comparison (Houtepen et al., Reference Houtepen, Heron, Suderman, Fraser, Chittleborough and Howe2020; Hughes et al., Reference Hughes, Bellis, Hardcastle, Sethi, Butchart, Mikton, Jones and Dunne2017; Straatmann et al., Reference Straatmann, Lai, Law, Whitehead, Strandberg-Larsen and Taylor-Robinson2020; Walsh et al., Reference Walsh, McCartney, Smith and Armour2019). Each ACE was assessed for both parents at ages three and five and was dichotomized (1 or 0) so that 1 refers to a positive case at either age or for either parent (see Table 1, Appendix). We utilized both parents reports of ACEs to comprehensively capture exposure to adversity. Scoring procedures for each ACE were adopted from a previous study of adverse childhood experiences using the MCS data (Straatmann et al., Reference Straatmann, Lai, Law, Whitehead, Strandberg-Larsen and Taylor-Robinson2020). Data were classified as missing if there was no information from both parents at both time points. ACE scores were calculated for individuals by summing the occurrence of each ACE, ranging from 0 to 7. The percentage of participants exposed to greater than 3 ACEs was low (e.g., approximately 3%). Similar to prior studies (Demkowicz et al., Reference Demkowicz, Panayiotou and Humphrey2021; Deniz et al., Reference Deniz, Humphrey, Demkowicz, Lereya and Deighton2023; Panayiotou & Humphrey, Reference Panayiotou and Humphrey2018), the low frequency high exposure ACE scores of 4 and greater were collapsed to a category of 3 or more ACEs which has a proportionally higher power of representation. Therefore, ACE scores were grouped into categories of 0 ACEs, 1 ACE, 2 ACEs, and 3 or more ACEs and were treated as count variables in the main effects and moderation models.
Table 1. Demographic information of cohort members
1 n (%); mean (SD).
School connectedness
School connectedness was measured at ages 11 and 14 based on self-report questions related to school. We selected items previously used to measure school connectedness and satisfaction from the MCS dataset (Arciuli & Emerson, Reference Arciuli and Emerson2020; Patalay & Fitzsimons, Reference Patalay and Fitzsimons2018). These questions also overlap with validated school connectedness measures such as the Student Engagement in Schools Questionnaire (Hart et al., Reference Hart, Stewart and Jimerson2011) and the school connectedness subscale of the School Climate Survey (Zullig et al., Reference Zullig, Koopman, Patton and Ubbes2010; Reference Zullig, Collins, Ghani, Patton, Scott Huebner and Ajamie2014). At both ages, students were asked the following questions related to school engagement: (1) How often do you try your best at school?, (2) How often do you find school interesting?, (3) How often do you feel unhappy at school?, (4) How often do you get tired at school?, and (5) How often do you feel school is a waste of time? Individuals responded to these questions on a 4-point scale ranging from 1 (“all of the time”) to 4 (“never”).
Cohort members answered how happy they were with their school at 11 and 14 (1 = “completely happy” to 7 = “not at all happy,” rescaled to 1 to 4 for consistency with remaining items). They were also asked about their friendships at school. At ages 11 and 14, they answered how many of their friends go to the same school as them (1 = “all of them” to 4 = “none of them”). All items were scored so that higher scores indicate positive associations with school.
Positive mental health
The Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS; Tennant et al., Reference Tennant, Hiller, Fishwick, Platt, Joseph, Weich, Parkinson, Secker and Stewart-Brown2007) was used to measure positive mental health at age 17. The WEMWBS has shown good content validity, test-retest reliability, and high correlations with other well-being scales (Tennant et al., Reference Tennant, Hiller, Fishwick, Platt, Joseph, Weich, Parkinson, Secker and Stewart-Brown2007). Adolescents completed the short 7-item scale and rated how often they felt certain experiences over the past two weeks (felt optimistic about the future, felt useful, felt relaxed, dealt with problems well, been thinking clearly, felt close to other people, and been able to make up their mind about things), ranging from 1 (“none of the time”) to 5 (“all of the time”). The items were summed to create a composite score, ranging from 7 to 35. The raw score was then transformed into a metric score using the short WEMWBS conversion table (Table 2 in the Appendix; Stewart-Brown et al., Reference Stewart-Brown, Tennant, Tennant, Platt, Parkinson and Weich2009).
Internalizing symptoms
Adolescent (age 14 and 17) internalizing symptoms were measured using the Strengths and Difficulties Questionnaire (SDQ; Goodman, Reference Goodman1997; Goodman et al., Reference Goodman, Meltzer and Bailey1998). The SDQ has good internal consistency (Goodman, Reference Goodman2001; Yao et al., Reference Yao, Zhang, Zhu, Jing, McWhinnie and Abela2009), moderate test-retest reliability (Yao et al., Reference Yao, Zhang, Zhu, Jing, McWhinnie and Abela2009), concurrent validity (Muris et al., Reference Muris, Meesters and van den Berg2003), and discriminant validity (Lundh et al., Reference Lundh, Wangby-Lundh and Bjarehed2008).
At age 14, parents completed the SDQ regarding their children’s behaviors, and at age 17, young people self-completed the questionnaires. The questions are the same for the parent-report and self-report SDQ. Self-report SDQ data was not recorded at age 14, and Booth et al. (Reference Booth, Moreno-Agostino and Fitzsimons2023) identified that parents reported lower levels of emotion symptoms, peer problems, and conduct problems than adolescents themselves on the SDQ within the MCS dataset. Parents or young people rated statements regarding adolescents’ feelings and behaviors in the past six months on a scale ranging from 1 (“not true”) to 3 (“certainly true”). Responses were recoded to a scale of 0–2 to align with standard SDQ scoring procedures. The internalizing score is composed of two subscales: emotional problems (i.e. “worries a lot”) and peer problems (i.e. “generally plays alone”). The total internalizing score ranges from 0 to 20, with higher scores indicating greater severity of internalizing problems.
Externalizing symptoms
Adolescent (age 14–17) externalizing symptoms were also measured using the SDQ (Goodman, Reference Goodman1997; Goodman et al., Reference Goodman, Meltzer and Bailey1998). Parents or young people rated statements regarding adolescents’ feelings and behaviors in the past six months ranging from 1 (“not true”) to 3 (“certainly true”). Responses were recoded to a scale ranging from 0 to 2 to follow standard SDQ scoring procedures. The externalizing score is the sum of five items from the conduct subscale (i.e. “often lies or cheats”) and five items from the hyperactivity scale (i.e. “constantly fidgeting or squirming”). The total externalizing score ranges from 0 to 20, with higher scores indicating greater severity of externalizing problems.
Covariates
Sex, ethnicity, and poverty were covariates in the analyses. These variables have previously been demonstrated to have significant associations with adolescent mental health outcomes (Ahmad et al., Reference Ahmad, McManus, Bécares, Hatch and Das-Munshi2022; Lai et al., Reference Lai, Wickham, Law, Whitehead, Barr and Taylor-Robinson2019; Yoon et al., Reference Yoon, Eisenstadt, Lereya and Deighton2023). Sex was dummy-coded so that 1 indicated male and 0 indicated female. Racial and ethnic minority was dummy-coded so that 1 indicated a racial or ethnic minority (mixed ethnicity, Indian, Pakistani and Bangladeshi, Black or Black British, and other ethnic groups), and 0 indicated white. Poverty was measured at ages three and five using equivalised income data. Families earning less than the 60% median income at either age were coded as a positive case of poverty (1), while families earning more were coded as 0.
Statistical analysis
Statistical analyses for the present study were structural equation models (SEMs), which allows the analysis of complex behavioral relationships through measurement models and structural models (Hair et al., Reference Hair, Anderson, Tatham and Black1998). SEM is advantageous to multiple regression due to its ability to analyze all variables in the model simultaneously and reduce measurement error (Nusair & Hua, Reference Nusair and Hua2010; Ullman & Bentler, Reference Ullman, Bentler and Weiner2012).
Data was cleaned and analyzed using R version 4.2.1. Confirmatory factor analysis (CFA) and main effects models were performed using the lavaan package in R (v0.6-13; Rosseel, Reference Rosseel2012). The semTools package (v0.5-6; Jorgensen et al., Reference Jorgensen, Pornprasertmanit, Schoemann and Rosseel2022) was used for simple slopes analysis and the latent interaction model according to the product indicator and residual centering method (Little et al., Reference Little, Bovaird and Widaman2006; Schoemann and Jorgensen, Reference Schoemann and Jorgensen2021).
The robust variant of Maximum Likelihood (MLR) estimation was used to perform CFA, the main effects model, and the moderation model. Rhemtulla et al. (Reference Rhemtulla, Brosseau-Liard and Savalei2012) found that MLR estimation yields useful test statistics to judge model fit and unbiased estimates of factor correlations when analyzing categorical variables. The non-normal distribution of product indicators used in the moderation model also calls for the use of the MLR estimator (Schoemann & Jorgensen, Reference Schoemann and Jorgensen2021).
Missingness was evaluated to determine if missing data were missing at random (MAR) or missing completely at random. The missingness pattern determined what techniques were appropriate to handle missing data (Rubin, Reference Rubin2004). A binary missingness variable was created so that 1 indicates that there was at least one missing focal variable (those measuring mental health at ages 14 and 17) and 0 indicating an observation with no missing data in the focal variables. A binomial regression was then run to test whether other variables (childhood adversity, poverty, and racial/ethnic minority status) predicted missingness. Significant associations between these predictor variables and missingness would indicate that the missing data were associated with observed variables and missing at random (MAR). If the data were MAR, missing data would be estimated using full information maximum likelihood (FIML) estimation (Kline, Reference Kline2016). FIML estimation is appropriate for data MAR and produces unbiased parameter estimates and standard errors when used for missing data estimation in SEM (Enders & Bandalos, Reference Enders and Bandalos2001; Lee & Shi, Reference Lee and Shi2021). Correlated error terms that improved model fit and were theoretically reasonable were included in the models (see Figures 1 & 2, Appendix).
The indices used to assess model fit for CFA and main effects models were robust RMSEA, robust CFI, robust TLI, and SRMR (Hu & Bentler, Reference Hu and Bentler1999). The standardized X 2 statistics were reported but are not used to judge model fit as the value is likely inflated due to the large sample size, resulting in the p-value not being an adequate measure of model fitness (Schermelleh-Engel et al., Reference Schermelleh-Engel, Moosbrugger and Müller2003). The following cutoffs were used to judge strong model fit: RMSEA < 0.05, CFI > 0.9, TLI > 0.9, and SRMR < 0.8 (Awang, Reference Awang2012; Forza & Filippini, Reference Forza and Filippini1998; Hair et al., Reference Hair, Anderson, Babin and Black2010; Hu & Bentler, Reference Hu and Bentler1999). These model fit indices were not used for the moderation model because they do not account for the dependence among observed variables from the product-indicator method and provide incorrect estimates of model fit (Schoemann & Jorgensen, Reference Schoemann and Jorgensen2021). Instead, model fit for the moderation models was assessed by utilizing the fit indices of the main effects models as a lower bound for the fit of its corresponding latent interaction model (Schoemann & Jorgensen, Reference Schoemann and Jorgensen2021).
Measurement models
Latent variables were created to measure school connectedness at age 11 and 14, externalizing and internalizing problems at age 14 and 17, and positive mental health at age 17. The latent variables included in the analysis were tested in one measurement model using CFA. Items were excluded from the latent variable factor structure if they had standardized loadings less than 0.4 (Kline, Reference Kline2016), indicating the item was not accurately measuring the construct. All reported factor loadings are standardized.
Main effects models
A main effects model was fit to examine the association between childhood adversity before the age of five and school connectedness at the ages of 11 and 14 as predictor variables and internalizing and externalizing problems at ages 14 and 17 and positive mental health at age 17 years as outcomes (Figure 1). All path estimates are standard YX loadings, meaning a 1 standard deviation change in the predictor results in some standard deviation change in Y. This model was designed to test if the predictor variables of childhood adversity and school connectedness at ages 11 and 14 years were significantly associated with the mental health outcomes measured at ages 14 and 17 years (RQs 1, 2a and 2b). The size and significance of coefficients representing the relationship between the predictor and dependent variables were used to evaluate support of the hypotheses.
Figure 1. Conceptual diagram of the main effects model. Childhood adversity at ages three and five, school connectedness at age 11, and school connectedness at age 14 were used as predictor variables for the outcomes of externalizing and internalizing problems at ages 14 and 17 and positive mental health at the age of 17. Racial and ethnic minority status, poverty, and sex were included as covariates but are not depicted in the figure for readability.
Moderation models
A latent interaction model was run to test whether school connectedness at ages 11 and 14 influenced the relationships between childhood adversity before the age of five and externalizing and internalizing problems (ages 14–17) and positive mental health (age 17). The moderation model was created using residual centering, a method that produces stable and interpretable model estimates by deriving the latent variable interaction from the observed covariation pattern among all indicators of the interaction, or product-indicators (Little et al., Reference Little, Bovaird and Widaman2006). Both moderators (school connectedness at age 11 and 14) were included in the model to account for interrelationships among the moderators and childhood adversity (Figure 2). All path estimates are standard YX loadings. The size and significance of coefficients representing the relationship between the interaction terms and dependent variables were used to evaluate whether school connectedness was a significant moderator (RQs 3a and 3b). If a significant moderation effect was found, it was probed using simple slopes analysis.
Figure 2. Conceptual diagram of the moderation model. School connectedness at age 11 and age 14 were examined as moderators to the relationship between childhood adversity and internalizing and externalizing problems at ages 14 and 17 and positive mental health at age 17. Black circles represent the interaction between school connectedness and childhood adversity. Racial and ethnic minority status, poverty, and sex were included as covariates but are not depicted in the figure for readability.
Simple slopes analysis
Significant interactions were investigated further and plotted using simple slopes analysis. The independent and dependent variables were plotted at values of the moderator (mean, one standard deviation [SD] above the mean, and one SD below the mean) to see how the moderator influences the slope. Simple slopes analysis facilitated an interpretation of the interaction between the predictor and moderating variables. If school connectedness was a protective factor, the slope between childhood adversity and mental health problems would decrease as school connectedness increased.
Results
Demographic information
After applying the exclusion criteria, 9,964 cohort members were included in the analysis (Table 1). Most cohort members experienced at least one ACE, with 35% experiencing two or more ACEs by the age of 5. The sample was nearly half male, majority white, and 38% of cohort members experienced childhood poverty. Bivariate correlations between study variables are shown in Table 2. Binomial regression testing the missingness variable found significant associations with predictor variables, indicating that the data were MAR. Therefore, missing data were handled using FIML.
Table 2. Zero order correlations of variables of interest
Note. *p < .05. **p < .01. ***p < .001.
Measurement model
The measurement model for the latent variables showed strong model fit (RMSEA = .038, RMSEA 90% CI [.036, .039], CFI = .948, TLI = .934, SRMR = .036, X 2 (276) = 3422.706). The strong model fit indicates that the latent variables’ constituent items accurately measured their respective constructs. However, for school connectedness at ages 11 and 14, the items asking about cohort members’ close friends attending the same school did not load well with the rest of the items (YX loadings < .4), indicating it was not related to the rest of the school connectedness items. These items were therefore removed from the model. The latent variables, indicators, and respective YX loadings of the final measurement model are summarized in Table 3.
Table 3. Factor loadings of the measurement model
Main effects model
The main effects model testing childhood adversity and school connectedness at ages 11 and 14 as predictors of adolescent mental health outcomes showed strong model fit (Figure 3). All effect sizes, standard errors, and significance test results for the main effects model are shown in Table 4.
Figure 3. Path diagram of main effects model. The path diagram shows associations between predictors of childhood adversity and school connectedness at ages 11 and 14 and adolescent mental health outcomes (externalizing problems, internalizing problems, and positive mental health). Racial and ethnic minority status, poverty, and sex were included as covariates but are not depicted in the figure for readability. Correlated error terms among the indicators of latent variables were also not depicted for readability but can be found in the appendix. *solid lines indicate significance at p < .05. Dashed lines represent non-significant relationships at p > .05. Coefficients of significant relationships are listed with the following significance levels: *p < 0.05, **p < 0.01, ***p < 0.001.
Table 4. Results of main effects and moderation structural equation models
Note. ACE = adverse childhood experience; MH = mental health; SC = school connectedness. *p < .05. **p < .01. ***p < .001.
Childhood adversity was associated with increased mental health risks throughout adolescence. Specifically, childhood adversity predicted both greater externalizing and internalizing problems at age 14 and greater externalizing problems and poorer positive mental health at age 17. Childhood adversity was not associated with internalizing problems at age 17 years.
School connectedness at ages 11 and 14 were promotive factors, significantly associated with all mental health outcomes. Age 11 school connectedness predicted fewer externalizing and internalizing problems at ages 14 and 17 and greater levels of positive mental health at age 17. Similarly, age 14 school connectedness was associated with benefits for all mental health outcomes but appeared to exhibit greater effect size estimates than age 11 school connectedness. These greater effect sizes suggest school connectedness at age 14 may be more strongly associated with mental health at ages 14 and 17.
The covariates of sex, childhood poverty, and racial/ethnic minority status showed significant relationships with the measured outcomes. Males were more likely to experience externalizing problems and have higher levels of positive mental health, while females were more likely to experience internalizing problems. Childhood poverty predicted greater externalizing and internalizing problems at age 14 and 17 but did not predict positive mental health at age 17. At age 14, racial and ethnic minorities were more likely to experience externalizing and internalizing problems. However, at age 17, racial and ethnic minority status predicted fewer externalizing and internalizing problems and did not predict positive mental health.
Moderation model
The moderation model showed strong model fit based on the fit indices of the main effects model (Figure 4). Although not used to assess model fit, the fit indices for the moderation model are reported in Table 4 along with the effect sizes and standard errors for each of the predictor variables.
Figure 4. Path diagram of moderation model. The path diagram shows associations between the predictors of childhood adversity and school connectedness at ages 11 and 14 and adolescent mental health outcomes (externalizing problems, internalizing problems, and positive mental health). Interactions between school connectedness and childhood adversity are represented by the black circles. Racial/ethnic minority status, poverty, and sex were included as covariates but are not depicted in the figure for readability. Correlated error terms among the indicators of latent variables were also not depicted for readability but can be found in the appendix. *solid lines indicate significance at p < .05. Dashed lines represent non-significant relationships at p > .05. Coefficients of significant relationships are listed with the following significance levels: *p<0.05, **p<0.01, ***p<0.001.
Age 14 school connectedness was not a protective factor against childhood adversity. In the moderation model, there were no significant interactions between age 14 school connectedness and childhood adversity for any mental health outcomes, indicating that school connectedness at this age did not influence the relationship between ACEs and mental health outcomes. School connectedness at age 11 significantly influenced the relationship between childhood adversity and externalizing and internalizing problems at age 14. The interaction between age 11 school connectedness and childhood adversity was significant for the outcome of externalizing problems at age 14 (β = −.082, SE = .025, p = .001), which indicates school connectedness moderated the relationship between childhood adversity and externalizing problems.
The simple slopes analysis revealed that age 11 school connectedness was a protective factor against childhood adversity for externalizing problems at age 14 (Figure 5). At each level of age 11 school connectedness tested (−1 SD, mean, +1 SD), the relationship between childhood adversity and externalizing problems was positive. However, the slope of the line was less steep when age 11 school connectedness was high (+1 SD; β = .102, SE = .037, p = .005) compared to when age 11 school connectedness was at its mean (β = .184, SE = .022, p < .001) or −1 SD (β = .266, SE = .033, p < .001). Greater levels of school connectedness at age 11 were associated with a weakened relationship between childhood adversity and externalizing problems at age 14. In other words, children with high ACE scores were less likely to exhibit externalizing problems when they felt strongly connected to school, suggesting that age 11 school connectedness is a protective factor.
Figure 5. Simple slopes analysis for age 11 school connectedness. Age 11 school connectedness was plotted at its mean (0) and ± 1 SD. The slope between childhood adversity both age 14 externalizing problems and age 14 internalizing problems becomes less steep at higher levels of age 11 school connectedness, indicating that age 11 school connectedness is a protective factor. Abbreviations: ACE = adverse childhood experience.
A similar protective effect was exhibited between school connectedness at age 11 and internalizing problems at age 14. The interaction between age 11 school connectedness and childhood adversity was significant (β = −.049, SE = .024, p = .039), indicating school connectedness significantly influenced the relationship between childhood adversity and internalizing problems at age 14. The relationship between childhood adversity and internalizing problems was positive at each level of age 11 school connectedness tested (−1 SD, mean, +1 SD). However, the slope of the line decreased at higher levels school connectedness, demonstrating how school connectedness weakened the relationship between childhood adversity and age 14 internalizing problems (+1 SD: β = .065, SE = .036, p = .068 ; mean: β = .114, SE = .022, p < .001; −1 SD: β = .162, SE = .032, p < 0.001). In fact, the slope of the line was not significantly different from 0 when school connectedness was 1 standard deviation greater than the mean, meaning high levels of school connectedness could neutralize the risk of developing internalizing problems associated with childhood adversity.
There was no significant interaction between age 11 school connectedness and childhood adversity for positive mental health, meaning school connectedness was not a protective factor for this outcome.
Discussion
Summary of key findings
In the current study, we examined the relationships between childhood adversity, school connectedness, and adolescent mental health outcomes. The key findings are that (a) childhood adversity was associated with increased risk for mental health problems at ages 14 and 17 years, (b) school connectedness is associated with better mental health outcomes across adolescence, (c) age 14 school connectedness appeared to have a stronger relationship with adolescent mental health outcomes compared to age 11 school connectedness, and (d) age 11 school connectedness was a protective factor against childhood adversity for age 14 externalizing and internalizing problems. These findings replicate previous studies that have identified childhood adversity and school connectedness as risk and promotive factors, respectively, for adolescent mental health (Arif et al., Reference Arif, Khan, Rauf and Sadia2019; Bevilacqua et al., Reference Bevilacqua, Kelly, Heilmann, Priest and Lacey2021; Blum et al., Reference Blum, McNeely and Rinehart2002; Choi et al., Reference Choi, Wang and Jackson2019; Frydenberg et al., Reference Frydenberg, Care, Chan and Freeman2009; Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021; Hardaway et al., Reference Hardaway, McLoyd and Wood2012; Healy et al., Reference Healy, Eaton, Cotter, Carter, Dhondt and Cannon2022; Huang & Baxter, Reference Huang and Baxter2021; James et al., Reference James, Jimenez, Wade and Nepomnyaschy2021; Kim, Reference Kim2013; Lensch et al., Reference Lensch, Clements-Nolle, Oman, Evans, Lu and Yang2021; Loukas et al., Reference Loukas, Roalson and Herrera2010; Loukas & Pasch, Reference Loukas and Pasch2013; Markowitz, Reference Markowitz2017; Raniti et al., Reference Raniti, Rakesh, Patton and Sawyer2022; Resnick et al., Reference Resnick, Harris and Blum1993, Reference Resnick, Bearman, Blum, Bauman, Harris, Jones and Udry1997; Schwerdtfeger et al., Reference Schwerdtfeger Gallus, Shreffler, Merten and Cox2015). The study makes a unique contribution to the literature as we find that age 11 school connectedness, but not age 14 school connectedness, moderates the relationship between childhood adversity and adolescent mental health, demonstrating a potential time-sensitivity to the protective nature of school connectedness. The subsequent sections discuss and compare these findings with previous literature.
Childhood adversity and adolescent mental health
Childhood adversity predicted greater externalizing problems at ages 14 and 17 and greater internalizing problems at age 14, in line with the hypotheses. These results are consistent with literature that have identified childhood adversity as a risk factor for adolescent mental health problems (Bevilacqua et al., Reference Bevilacqua, Kelly, Heilmann, Priest and Lacey2021; Choi et al., Reference Choi, Wang and Jackson2019; Healy et al., Reference Healy, Eaton, Cotter, Carter, Dhondt and Cannon2022; James et al., Reference James, Jimenez, Wade and Nepomnyaschy2021). Childhood adversity also predicted worse positive mental health at 17. Consistent with the present findings, Goetschius et al. (Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021) found that childhood violence and social deprivation each predicted lower positive functioning at age 15. This study is the only one of which we are aware that examines positive mental health as an outcome related to early life adversity. Our study extends these results by demonstrating the distal effects of ACEs through a significant negative association with positive functioning at age 17.
Contrary to the hypothesis, childhood adversity did not predict greater internalizing problems at age 17. Anderson et al. (Reference Anderson, Siciliano, Henry, Watson, Gruhn, Kuhn and Compas2022) also found higher ACE exposure was not related to adolescent internalizing symptoms, although the participants were 10–15 years old rather than 17. These findings could be explained by the exclusion of sexual abuse and neglect in our measure of childhood adversity, as they are strong predictors of internalizing problems (Giano et al., Reference Giano, Ernst, Snider, Davis, O’Neil and Hubach2021; Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021). Conversely, it is possible that the risk of internalizing problems associated with ACEs decreases over time. Studies have identified that childhood adversities are stronger predictors of internalizing problems at earlier ages of adolescence relative to subsequent years (Gilman et al., Reference Gilman, Kawachi, Fitzmaurice and Buka2003; Jaffee et al., Reference Jaffee, Moffitt, Caspi, Fombonne, Poulton and Martin2002; Nweze et al., Reference Nweze, Ezenwa, Ajaelu and Okoye2023; Oldehinkel & Ormel, Reference Oldehinkel and Ormel2015). This pattern of decreased risk of internalizing symptoms in late adolescence from childhood adversity could explain these findings. Lastly, internalizing problems were measured using the parent-report SDQ at age 14 and self-report SDQ at age 17. Adolescents from the MCS dataset have been found to report more mental health difficulties than their parents on the SDQ (Booth et al., Reference Booth, Moreno-Agostino and Fitzsimons2023), indicating that it is likely the different informant structures measured different aspects of cohort members’ internalizing behaviors, which could explain these findings.
School connectedness as a promotive factor
School connectedness is a promotive factor of adolescent mental health. Both age 11 and age 14 school connectedness predicted all mental health outcomes with the expected directionality: a negative relationship with externalizing and internalizing problems at ages 14 and 17 and a positive relationship with positive mental health at age 17. These results replicate prior findings that show school connectedness benefits adolescent externalizing problems (Blum et al., Reference Blum, McNeely and Rinehart2002; Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021; Hardaway et al., Reference Hardaway, McLoyd and Wood2012; Loukas et al., Reference Loukas, Roalson and Herrera2010; Loukas & Pasch, Reference Loukas and Pasch2013; Resnick et al., Reference Resnick, Harris and Blum1993, Reference Resnick, Bearman, Blum, Bauman, Harris, Jones and Udry1997), internalizing problems (Huang & Baxter, Reference Huang and Baxter2021; Kim, Reference Kim2013; Lensch et al., Reference Lensch, Clements-Nolle, Oman, Evans, Lu and Yang2021, Markowitz, Reference Markowitz2017, Raniti et al., Reference Raniti, Rakesh, Patton and Sawyer2022; Schwerdtfeger et al., Reference Schwerdtfeger Gallus, Shreffler, Merten and Cox2015), and positive mental health (Arif et al., Reference Arif, Khan, Rauf and Sadia2019; Frydenberg et al., Reference Frydenberg, Care, Chan and Freeman2009; Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021). Overall, these findings suggest that school connectedness promotes adolescent mental health without respect to risk status.
The present study demonstrates the potential long-term benefits of school connectedness while controlling for the effects of developmental timing. Age 14 school connectedness appeared to have stronger associations with all mental health outcomes measured at age 17 relative to age 11 school connectedness, revealing the importance of timing. While the closer proximity between age 14 and 17 likely contributes to the stronger effect sizes of age 14 school connectedness, it is also possible that the specific school environment and developmental differences at age 14 could contribute to these findings. At age 14, students in the UK begin to study for General Certificates of Secondary Education (GCSEs), important qualifications that are major stressors for students (Department for Education, 2023; National Education Union, 2019). Furthermore, individuals rely less on their family social networks as they transition to adolescence and are less likely to receive social support from teachers, which could increase the importance of school-related social support at age 14 (Eccles et al., Reference Eccles, Midgley, Wigeld, Buchanan, Reuman and Flanagan1993; Goodenow, Reference Goodenow1993; Nelson et al., Reference Nelson, Jarcho and Guyer2016; Oelsner et al., Reference Oelsner, Lippold and Greenberg2011). These differences between ages 11 and 14 may contribute to how school connectedness varies at these time points.
The long-lasting promotive effects of school connectedness are another novel finding from this study. Age 11 school connectedness predicted mental health outcomes at ages 14 and 17, even when controlling for age 14 school connectedness. These findings demonstrate that feeling connected to school at age 11 has positive benefits to mental health three and six years later, highlighting school connectedness’s potential as a target to promote students’ mental health in the long-term.
School connectedness as a protective factor
School connectedness at age 11 was found to be a protective factor against childhood adversity in relation to externalizing and internalizing problems at age 14. The moderation model and simple slopes analysis revealed that higher levels of school connectedness were associated with a weakened relationship between ACEs and externalizing and internalizing problems at age 14. Previous studies have also found a moderating effect of school connectedness and extracurricular participation on the relationship between childhood adversity and externalizing problems in adolescence (Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021; Hardaway et al., Reference Hardaway, McLoyd and Wood2012; Loukas et al., Reference Loukas, Roalson and Herrera2010; Loukas & Pasch, Reference Loukas and Pasch2013).
The protective effect of age 11 school connectedness against internalizing and externalizing problems is in line with the neurocognitive transactional model of childhood adversity. The model suggests that disrupting social thinning and stress generation would decrease the negative mental health outcomes associated with childhood adversity (McCrory et al., Reference McCrory, Foulkes and Viding2022). Given that school connectedness consists of feelings of belonging, social support, and positive engagement with the school environment (Hodges et al., Reference Hodges, Cordier, Joosten, Bourke-Taylor and Speyer2018; Libbey, Reference Libbey2004), it is tenable that high levels of school connectedness would offset or disrupt the negative cascade by counteracting social thinning and stressful experiences, and in turn, reduce negative mental health consequences. The moderating effect of age 11 school connectedness between childhood adversity and internalizing and externalizing outcomes lends support to this theory.
Contrary to the hypothesis, age 14 school connectedness did not moderate the relationship between childhood adversity and mental health outcomes at ages 14 and 17. These results suggest that school connectedness may only be protective among younger students who have experienced adversity more recently.
Strengths and limitations
Strengths of this study include its large sample size, multiple validated measures of mental health, longitudinal design, and the use of SEM. SEM facilitated analysis of the relationships between all the variables in the model simultaneously rather than in succession, as would be the case in multiple regression analysis (Hair et al., Reference Hair, Anderson, Tatham and Black1998). This method, longitudinal data, and adequate statistical power from the large sample size facilitated a better understanding of the interrelationships between the variables of interest. A major strength of this study was the inclusion of moderators and mental health outcomes at multiple time points, facilitating the analysis of the proximal and distal effects of school connectedness, childhood adversity, and their interactions. Testing multiple moderators simultaneously contributes to a more parsimonious model and provides the most detailed analysis of how the moderators interact with each other and predictor variables (Montoya, Reference Montoya2019). Furthermore, there are distinct differences in development and school environments between the ages of 11 and 17, and the repeated-measures longitudinal approach helped elucidate how these differences may affect children’s mental health.
A number of limitations should be borne in mind when interpreting the findings of this study. The study was limited by its correlational design and the measurement of childhood adversity and school connectedness. Although the longitudinal design limits the confounding of variables, the findings are correlational, meaning causal inferences cannot be drawn. At age 14, the directionality of the association between school connectedness and mental health outcomes cannot also be ascertained. ACE scores in the present study did not include physical or emotional neglect, sexual abuse, or having an incarcerated relative, each of which are associated with mental health problems in adolescence. Additionally, ACEs were measured as a cumulative exposure to childhood adversity, and there has been movement in the field to measure specific dimensions of adversity, such as deprivation and violence (Goetschius et al., Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021). More nuanced and comprehensive measurement of childhood adversity in future studies will facilitate a clearer understanding of its impact on adolescent mental health and interactions with school connectedness.
The school connectedness measure was also not comprehensive. Since the MCS dataset did not use a validated measure of school connectedness, relevant items in the questionnaire that overlapped with validated scales (Student Engagement in Schools Questionnaire [Hart et al., Reference Hart, Stewart and Jimerson2011] and the School Climate Survey [Zullig et al., Reference Zullig, Koopman, Patton and Ubbes2010; Reference Zullig, Collins, Ghani, Patton, Scott Huebner and Ajamie2014]) were selected for CFA. In this process, the items related to having friends at school did not load well with the rest of the questions and were removed. In doing so, the school connectedness measure centered around school satisfaction and engagement, important components of school connectedness, but was likely not fully representative of the construct. Specifically, the affective dimension of acceptance and the cognitive dimension of cohort members’ perceptions of quality peer relationships were not captured. The school connectedness measure employed by Goetschius et al. (Reference Goetschius, McLoyd, Hein, Mitchell, Hyde and Monk2021) comprised of questions centered on feelings of belongingness and also found that school connectedness was protective against externalizing problems. Future studies should aim to use a validated scale for school connectedness that fully captures the cognitive, affective, and behavioral domains of the construct.
Lastly, the parent- and self-report SDQ at ages 14 and 17, respectively, are likely to measure different aspects of young people’s internalizing and externalizing problems. The measures at both time points are not assumed to be invariant, a limitation of this study. Booth et al. (Reference Booth, Moreno-Agostino and Fitzsimons2023) found that adolescents reported more negative outcomes on the SDQ relative to their parents, suggesting the informant discrepancy may be associated with measurement invariance. Future studies should aim to account for measurement invariance. However, previous work using the MCS dataset (Toseeb et al., Reference Toseeb, Oginni, Rowe and Patalay2022) demonstrates that the factor structure of parent-report SDQ is not the same at ages 14 and 17 years, indicating that parent-report SDQ is likely to be variant across these ages. Based on these findings, we chose to use parent-report SDQ at age 14 due to availability of data and self-report SDQ at age 17 years.
Conclusion
In summary, we found that childhood adversity and school connectedness were risk and protective factors, respectively, for adolescent mental health among UK youth. Moreover, we discovered that school connectedness at age 11 significantly moderated the relationship between childhood adversity and age 14 internalizing and externalizing problems, a novel finding demonstrating the protective effects of feeling connected to school. School connectedness at age 14 was not protective, suggesting that school connectedness at younger ages may disrupt processes that link childhood adversity to externalizing problems in adolescence. While promoting school connectedness throughout all levels of education is important, these differences can inform targeted interventions aimed at supporting children who have experienced adversity.
School connectedness is a malleable factor that schools should promote. Classroom content relevance, whole school approaches, strong school counselor relationships, and supportive classroom management strategies are linked to increased feelings of school connectedness (Acosta et al., Reference Acosta, Chinman, Ebener, Malone, Phillips and Wilks2019; Chapman et al., Reference Chapman, Buckley, Sheehan and Shochet2013; Kiefer & Pennington, Reference Kiefer and Pennington2016; Kim & Cappella, Reference Kim and Cappella2016; Martin & Sorensen, Reference Martin and Sorensen2020). Based on the findings from the present study, these interventions may protect students against the negative effects of childhood adversity on internalizing and externalizing problems and benefit all students’ mental health and well-being.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0954579424001184.
Acknowledgments
We are grateful to the Centre for Longitudinal Studies (CLS), UCL Social Research Institute, for the use of these data and to the UK Data Service for making them available. However, neither CLS nor the UK Data Service bear any responsibility for the analysis or interpretation of these data.
Funding statement
None.
Competing interests
None.
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