Examining the Relationships Between Adverse Childhood Experiences,Student Generational Status,and Exam Performance in Emerging Adult Undergraduates

Abstract

Objectives: We aimed to understand how adverse childhood experiences (ACEs) correlated with undergraduates’ examination performance, while looking at the contributions of attentional control and resilience. Methods: Students aged 18–25 years were recruited from first-year Psychology classes (Total N = 488). ACE scores, as well as the Attention Control Scale and the Brief Resilience Scale, were measured in conjunction with students’ first exam score of the semester. Results: Participants with no (0) and high (4+) ACE burden had higher resilience compared to those with moderate (1–3) ACE burden. Higher examination scores related to greater levels of attentional control, but no relationship was found with resilience or ACEs. Conclusions: Contrary to predictions, ACEs were related to resilience but in a non-linear fashion. Students with highest ACE burden require further study to understand factors contributing to their academic and personal resilience.

Keywords

adverse childhood experiences emerging adulthood resilience attentional control first generation students

“Emerging adulthood” is a developmental period from ages 18–25 that marks the transition from adolescence to adulthood (Arnett, 2000). While young persons may have more autonomy and less structure during this period in their lives, pressure to succeed in education, work, romance, and parenthood seems to be at an all-time high (Arnett et al., 2020). Emerging adults entering higher education face particular challenges. They are often leaving their friends and families for the first time. They experience changes in sleep and nutrition, heavy financial burden, and levels of expectation implemented by higher education that are often like none they have experienced before (Karatekin, 2016). Psychological health during emerging adulthood is found to be a strong indicator of success, health, and well-being later in life (Masten et al., 2004). Unfortunately, stress (Frazier et al., 2019) and depression (Newcomb-Anjo et al., 2017) are regularly occurring themes in research within the emerging adult population, including students. Poor mental health is a concern in and of itself, but can it also negatively impact academic outcomes as well (e.g., Duffy et al., 2020; Shankar & Park, 2016). Understanding the correlates of emotional well-being, and their impact on cognitive function in the emerging adult undergraduate population, is critical to promote academic success, and may inform interventions that could support well-being in this population.

Adverse Childhood Experiences

One potential determinant of university students’ psychosocial and academic function is adverse childhood experiences, or ACEs (Lu et al., 2017; Taylor et al., 2019; Windle et al., 2018). Although a consistent working definition has yet to be agreed upon, ACEs are often broadly classified as representing child maltreatment (i.e., abuse and neglect) and household dysfunction (e.g., living with a parent/caregiver with mental illness, witnessing violence in the home (Felitti et al., 1998). ACEs are fairly prevalent within Western societies. For example, a comprehensive review of 32 Canadian studies was conducted by Public Health Ontario, which identified ACE prevalence (i.e., the presence of at least one ACE) as ranging between 50% to 65% (Carsley & Oei, 2020). ACEs have been found to negatively impact academic success (Windle et al., 2018) and academic engagement (Bethell et al., 2014). Below, we briefly present two pathways by which ACEs may have deleterious effects on students’ academic success.

Psychosocial Impacts of ACEs. The effects of early adversity on adult mental and physical health are well documented (e.g., Felitti, 2002; Felitti et al., 1998; Karatekin, 2016). ACEs may also have a direct bearing on the developmental tasks of emerging adulthood, including difficulty forming and maintaining healthy relationships (Windle et al., 2018), as well as under-achievement in academics (Nurius et al., 2013). Hopelessness and negative core schema, strongly linked to depression, have been found more commonly in emerging adults with ACEs opposed to those without (Liu et al., 2012). Less-developed coping mechanisms and lack of interest in school or work were also found to be correlated with, and indicators of, depression in emerging adults (Newcomb-Anjo et al., 2017). Risk of poor outcomes increases as ACE burden increases, with prior studies suggesting four or more ACEs conveying highest risk for mental and physical health conditions (e.g., Felitti et al., 1998). Yet Karatekin (2016) found that college students with as few as two or more ACEs showed much greater susceptibility to depressive symptoms, anxiety disorders, and or suicidal ideation over the duration of the school term, when compared to those with one ACE.

ACEs and Brain/Cognitive Development. As noted above, mental health can have a direct impact on academic success; yet exposure to early adversity can also impact brain development in deleterious ways that could, in turn, have negative impacts on cognitive ability. This indicates another mechanism by which ACEs could negatively impact emerging adult students. Greater exposure to ACEs appears to increase likelihood of forming atypical neural connections (Teicher & Samson, 2016) and conditions of neurodivergence (Lu et al., 2017). Some of the brain areas that seem to be particularly vulnerable to early adversity include the anterior cingulate gyrus, a common structure in attention, emotional, and cognitive control (Petersen & Posner, 2012), as well as the hippocampus, an important structure in the creation and retrieval of memories (Teicher & Samson, 2016). Attentional control, a higher-order aspect of attention that is sometimes considered to be a component of executive functioning, is comprised of the ability to focus, as well as the ability to shift attention readily and fluently between tasks. Taylor et al. (2019) suggest that persons with higher ACEs may have poor attention control, and those persons may have to deploy more mental resources in order to maintain similar performance to their neurotypical counterparts. Attention is a critical function insofar as it provides a foundation for other critical cognitive abilities, such as memory and executive function (Sohlberg & Mateer, 2001), both of which are needed to function effectively in university. In addition, Taylor et al. (2019) further found that repetitive negative thinking mediated the role between ACEs and anxiety in college students, and that this relationship was moderated by attentional control. Thus it is possible that attention problems are not only an assay of impaired cognitive development following ACEs, but may have downstream effects on mental health.

ACEs in First-Generation Students. “First-generation (FG) student” refers to individuals who are the first generation of their nuclear families to attend and complete 4 years of tertiary education (OECD, 2018). FG students may be more likely than continuing generation (CG) students to have experienced ACEs, one reason being that they often come from lower socioeconomic (SES) families (i.e., from the working class or poverty) (OECD, 2018). For example, unintentional neglect may occur as a result of over-worked caregivers holding multiple jobs, and difficulty obtaining basic necessities (e.g., food, shelter, clothing). Reduced resources mean families must live in less safe neighborhoods; in order to protect their children, lower SES parents may adopt a more authoritarian parenting style, characterized by less warmth and a more restrictive or disciplinary approach (Rankin & Quane, 2002). Rubin and Kelly (2015) found that lower SES undergraduates endorsed this kind of parenting style which, in turn, inhibited the development of socially-supportive friendships and emotional well-being. Academia tends to exemplify an upper-middle-class cultural and value system, and FG students may feel out of place and have difficulty accessing supportive resources in an unfamiliar environment (Phillips et al., 2020; Stephens et al., 2012). This may make them uniquely vulnerable to the effects of stress, interacting with a higher exposure to ACEs coming in to university.

The Mitigating Impact of Resilience

Stress and challenge are known to be part and parcel of emerging adulthood, yet the ability to cope effectively with such stressors may be hampered by ACE exposure. Resilience is often broadly defined as the ability to “bounce back” from difficult situations, and may mitigate the impact of ACEs on well-being (Morgan et al., 2022). Resilience may appear differently across different contexts – for example, in an academic environment, resilience may be operationalized as the ability to manage academic stressors and overcome discouraging situations (Ungar et al., 2007). Okwori (2021) gathered survey data on 570 college students to examine further the relationships between ACE scores, resilience, and other variables. As predicted, they found an inverse relationship between ACEs and resilience, and also that demographic factors such as gender, being urban versus rural, and being raised with both parents influenced ACE reporting. Although not formally classified as an ACE, attachment anxiety associated with one’s parents was also found to negatively predict resilience in female college students (Robbins et al., 2018).

ACEs may lower a student’s resilience in coping with the transition to and success in higher education. Conversely, however, ACEs are not deterministic and in fact one’s sense of resilience might act as a buffer against their negative effects. Arnett (2016) discussed the possibility that emerging adults embarking on the “college dream” may actually have higher levels of mental health in comparison to their peers, as it is difficult to be a successful university applicant without some baseline level of well-functioning. This may suggest a difference of psychological functioning in student emerging adults, in comparison to those who do not attend tertiary education (Arnett, 2016). Perhaps what underlies this higher level of psychological functioning is that they have higher levels of resilience. For example, Bethell et al. (2014) reported that children (17 years or younger) were 2.67 times more likely to be required to repeat a grade if they had experienced two or more ACEs than students with zero or one ACE, and 2.59 times less likely to be actively and consistently engaged in school. These results were however mitigated when the children had high levels of resilience. This suggests that those who enter tertiary levels of education, particularly those with two or more ACEs, may have underlying protective factors, such as high level of resilience. In fact, resilience may be one of the key mechanisms by which students with high ACEs fair better or worse in academia. In further research from an Eritrean sample of college students, Kelifa et al. (2021) examined ACEs, resilience, depression symptoms, and subjective well-being using questionnaire data. While ACEs were negatively associated with resilience, they also found that resilience was a mediator of the impact of ACEs on subjective well-being.

Summary and Hypotheses

The study had three primary hypotheses. Our first (exploratory) hypothesis was to quantify the overall self-report of ACEs in our sample relative to population estimates. Second, we hypothesized that FG students would have higher ACE burden compared to CG students. Third, consistent with the dose-response effect of ACEs, we hypothesized that increasing ACEs would be associated with worse exam scores.

We had two secondary hypotheses related to resilience and attentional control. Our fourth hypothesis was that there would be an inverse linear relationship with ACE burden and resilience, and if significant, then resilience would serve to mediate the relationship between ACEs and examination score. Our fifth and final hypothesis was similar, where we anticipated an inverse linear relationship between ACE burden and attentional control, and if significant, then attentional control would mediate the relationship between ACEs and examination score, with higher levels of attentional control sharing a positive relationship with examination score. Attentional control served as a proxy for overall brain/cognitive function, given its sensitivity to neurological insult (including from ACE exposure), as well as its importance for being able to attend to and learn new information.

Methods

Participants

The current report is part of a larger parent study examining the impact of self-regulation training on undergraduate students enrolled in Introductory Psychology classes. Baseline data were collected and examined from students within the emerging adulthood range (i.e., 18–25 years) who were in the second semester of a first-year Psychology course (i.e., Psychology 100B: Introductory Psychology II) at a mid-size Canadian institution in the Pacific Northwest. We specifically chose second semester to conduct the study, as we felt that the very first semester at university is a unique time with many confounding variables that could obscure our results. Rather than predetermine our sample size, we attempted to recruit as many participants as possible from the course, in order to gain the most representative sample. Out of a possible 683 students enrolled in PSYC 100B, there were 516 participants that consented and provided fully useable data. However, of these participants, eight were excluded because they were older than the specified age range and 20 were excluded because they did not include their age in the survey, leaving a total of 488 participants. Approval from our institution’s Human Research Ethics Board (HREB) was obtained for this study, prior to data collection.

Our sample was large and considered representative of our student body. As seen in Table 1, participants were primarily female, with Caucasian and Asian-identified students most prevalent. Mean age of participants was 19.14 years (SD = 1.056). Eighty-four students (17%) identified as FG. We also inquired about employment status, given that FG students may be more likely to need outside employment, and work demands could impact time available to study and, therefore, exam performance. Employment status did not significant differ between FG and CG students (employment status χ² (2, N = 461) = 1.49, p = .47. In addition, exam score did not significantly differ between FG (M = 72.23, SD = 16.70) and CG (M = 74.87, SD = 15.34), t (472) = −1.41, p = .16. Using univariate ANOVA, there were no significant difference in examination scores (looked at as a continuous variable) as a function of employment status F (2,456) = 0.990, p = .372).

Table 1.

Demographic Characteristics of Sample and Between Generational Status.

Characteristic	Group	Entire Sample	FG	CG
N		488	84	396
Age (M, SD)		19.14, 1.506	19.26, 1.804	19.11, 1.426
Gender	Female	72.7%	83.3%	71.2%
	Male	27.3%	14.3%	27.8
	Other	1.20%	0.00%	1.6%
Ethnicity	Caucasian	75.2%	78.6%	75.5%
	Asian	19.1%	20.3%	18.9%
	Middle Eastern	2.5%	2.4%	2.5%
	Hispanic	1.8%	1.2%	2.0%
	Other	1.2%	1.2%	1.3%
Employment	Full-time	0.4%	1.2%	0.3%
	Part-time	37.3%	36.9%	38.1%
	Unemployed	57.4%	58.3%	57.6%

Self-Report Measures

Demographics Questionnaire

For the parent study, we gathered comprehensive demographic data such as age, sex, gender, race/ethnicity, lifestyle, experience of trauma, and exposure to ACEs. Responses consisted of both closed and open-ended answers, depending on the question asked. A subset of those data is presented in the current report. FG status was coded based on the following question: “Are you the first member of your family to attend university?” (Y/N response).

Adverse Childhood Experiences

Our HREB had concerns about presenting the original ACE questionnaire (Felitti et al., 1998) to such a large participant pool where individualized follow-up, if necessary, would not be practical. In particular, because many people in our sample would be close enough to childhood age (i.e., <18), this could trigger additional responsibilities regarding mandated reporting of abuse because the first author is a clinical psychologist and mandated reporter. After consultation with our HREB and another colleague in our department conducting similar research, we modified the original ACE scale for the purposes of this study. First, we did not specifically use the “prior to age 18” referent that was included in the original ACE scale, although we included a preamble that referenced “Child Protective Services”, and included a separate question that asked about adult traumatic events. Second, we modified two of the original ACE items, specifically the ACE related to neglect and the ACE related to caregiver abuse. Our modified ACE scale is presented in Table 2, with reference to how our items map to the original items in Felitti et al.’s (1998) ACE questionnaire.

Table 2.

Modified ACE Questionnaire (Items from the Original ACE Questionnaire Parenthetical).

We subjected our measure to dichotomous dummy coding such that each ACE was coded as 0 = no, 1 = yes, tallied, and then recoded into three groups, using Felitti et al.’s (1998) determination of risk groups. Specifically, a score of 0/9 resulted in a code of zero, indicating no ACE presence. A score of 1–3/9 resulted in a code of one, indicating moderate ACE presence. A score of 4–9/9 resulted in a code of two, indicating high ACE presence. We also looked at ACE type, separating childhood maltreatment (i.e., physical/sexual/emotional abuse and neglect) from household dysfunction (e.g., witnessing family violence, caregiver mental illness or suicide). It is difficult to provide reliability standards for the ACE questionnaire, as its psychometrics have been relatively understudied (Zanotti et al., 2018), often the focus is on test-retest reliability rather than internal consistency (e.g., Kalmakis et al., 2020; Pinto et al., 2014), and when statistics are reported, these are often for a researcher-modified version of the scale (Karatekin & Hill, 2019). A recent study on college students reported internal consistencies ranging from 0.66 to 0.74 (Zanotti et al., 2018). The internal consistency of the ACE measure applied in the current paper was determined to be .67.

Brief Resilience Scale

The Brief Resilience Scale (BRS; Smith et al., 2008) is a six-item, self-report questionnaire, where resilience is defined as the ability to bounce back from stress. This measure uses a five-point Likert scale (1 = strongly disagree to 5 = strongly agree), where the six items (and three are reverse coded) are averaged to create a mean resilience score from one to five (higher score indicates higher resilience). The BRS was found to have good test-retest reliability and internal consistency (α = .80–.91), with resilience scores in undergraduate students ranging from 3.53 to 3.98, as reported by Smith et al., (2008). The internal consistency in our sample falls within the expected range (α = .81).

Attentional Control Scale

The Attentional Control Scale (ACS; Derryberry & Reed, 2002) is a 20-item self-report questionnaire of the ability to regulate one’s attention. This measure is comprised of a four-point scale (1 = almost never to 4 = always, with 11 items reverse coded) to measure attentional control; scores can range from 20 to 80. This scale has two subscales, attention shifting and attention control, in addition to the total score. The ACS items are tallied, with a greater score equaling higher levels of attentional control. The measure has been found to have good internal consistency (α = .84, as reported by Ólafsson et al., (2011)), and attentional scores for undergraduate students ranging from 46.4 to 58.1, with a median score of 52.5 (Derryberry & Reed, 2002). The internal consistency in our sample falls within the expected range (α = .81).

Procedure

Participants were recruited from three sections of Psychology 100B, occurring in the spring semester. A combination of in-class announcements, as well as announcements on the university’s learning management system, were used to disseminate information about the study. As part of study consent, students could choose to opt in to have their survey data analyzed, or decline to have their data reviewed. Students were also asked to consent to have their examination grade recorded as part of the study.

Statistical Analysis

Analyses were conducted using IBM Statistics SPSS 27. Descriptive statistics were examined and we determined that measures were normally distributed with no significant outliers, unless otherwise stated. Alpha level .05 was used (two-tailed) unless otherwise indicated. Assumptions of homogeneity were met using Levene’s test of significance (p ≥ .05), unless otherwise stated. If an assumption was violated, non-parametric tests were employed. Pairwise deletion was applied to missing data, in order to maximize our available sample size for each analysis.

Results

ACEs and Their Impact

ACE Burden Across the Sample

Our first hypothesis pertained to ACE burden across the entire sample. Results indicated that 36% (N = 162) of participants reported zero ACEs, 48% (N = 216) experienced moderate ACEs, and 16% (N = 70) experienced high ACEs. Our second hypothesis pertained to differences in ACE burden as a function of generational status. Using chi-square analysis, we found that, as predicted, FG and CG students differed in overall ACE burden, with more FG students endorsing at least one ACE (77%) compared to CG students (61%) χ² (8, N = 448) = 16.03, p = .042.

ACE type across the sample

We conducted supplemental exploratory analyses using multivariate ANOVA to determine whether the specific type of ACE (i.e., childhood maltreatment vs. household dysfunction) differed across the sample and also as a function of generational status. Assumptions were met using Box’s test of equality of covariances matrices (Box’s M = 4.451, p = .221). A statistically significant main effect of ACE type across the sample was found using Wilk’s lambda, F (1,446) = 12.886, p < .001, albeit with a small effect size (η2 = 0.28). Household dysfunction ACEs (M = 0.9152, SD = 1.18) occurred at a higher frequency in the sample overall, compared to that of childhood maltreatment ACEs (M = 0.6741, SD = 0.98). A statistically significant main effect of generational status was also found, F (1,446) = 5.582, p = .019, to the effect that, overall, FGs were at higher likelihood of experiencing both ACE types, again with a small effect size (η2 = 0.012). There was no evidence of a significant interaction between generational status (FG, CG) and ACE type (i.e., childhood maltreatment vs. household dysfunction; F (1,446) = 0.280, p = .597). We conducted follow-up chi-square analyses to look at each of our nine individual ACEs and whether they differed between FG and CG students. None were statistically significant (Tables 3 and 4).

Table 3.

Descriptive Statistics of ACE type Across and Between Generational Status.

Type of ACE	Status	N	M	SD
Household dysfunction	FG	75	1.1733	1.29837
	CG	373	0.8633	1.14538
	Entire sample	448	0.9152	1.17656
Childhood maltreatment	FG	75	0.8667	1.01786
	CG	373	0.6354	0.97055
	Entire sample	448	0.6741	0.98128

ACE Impact on Examination Scores

To test our third hypothesis – the impact of ACE dose on examination scores – a univariate ANOVA was used. No significant difference was found between ACE burden groups in terms of examination performance, F (2,446) = 0.009, p = .991). Zero ACEs (M = 74.25, SD = 17.06), moderate ACEs (M = 74.43, SD = 15.08), and high ACEs (M = 74.19, SD = 13.89) had very similar mean examination scores, all of which were equivalent to a B average at our university’s grading scale.

Table 4.

ACE Characteristics Across and Between Generational Status (Items Captured from the Original ACE Questionnaire Presented Parenthetically).

Endorsed ACE Measures	Endorsed by Everyone (%)	Endorsed by FG (%)	Endorsed by CG (%)
1. Neglect (ACEs 4 and 5)	11.10	10.70	11.36
2. Physical abuse (ACE 2)	9.40	11.90	8.84
3. Emotional abuse (ACE 1)	30.30	38.10	28.79
4. Sexual abuse, inappropriate touching (ACE 3)	15.20	20.20	14.39
5. Witnessing parents or guardians subjected to sexual, emotional, or physical abuse (ACE 7)	19.30	26.20	18.41
6. Experienced their parents or guardians get divorced or separated (ACE 6)	23.00	27.40	22.22
7. Lived in house with alcoholic or abusive drinker (ACE 8)	16.60	21.40	16.15
8. Lived with someone mentally ill, or someone who attempted suicide (ACE 9)	25.60	34.50	24.68
9. Experienced a family member or caretaker going to prison (ACE 10)	4.70	6.00	4.59
ACE type category totals
Childhood maltreatment	40.6	50	38.89
Household dysfunction	50.0	61.9	48.26

Understanding the Influences of Resilience and Attentional Control

Resilience, ACE Burden, and Examination Score

Our fourth hypothesis was that there would be a negative linear relationship between ACE burden and resilience, meaning resilience would decrease as ACE burden increased. There were no differences between FG (M = 3.05, SD = .70) and CG (M = 3.17, SD = .70) in resilience scores, t (477) = −1.44, p = .15. A univariate ANOVA was conducted to determine if resilience scores differed between ACE burden categories (zero, moderate, high). The omnibus test was significant (F (2,451) = 5.876, p = .003, n² = 0.025). Planned comparisons were used to evaluate differences between no ACEs versus moderate ACEs, moderate ACEs versus high ACEs, and no ACEs versus high ACEs. T-tests were employed to determine the nature of differences between the groups, with a more stringent alpha level of p < .025 to correct for planned comparisons. As predicted, participants with zero ACEs (M = 3.2689, SD = 0.65,114) had significantly higher resilience scores than those with moderate ACEs (M = 3.0436, SD = 0.73,513; t (382) = 2.915, p = .0024, Cohen’s d = 0.324). However, contrary to our prediction, participants with moderate ACEs had significantly lower resilience scores than those with high ACEs (M = 3.2662 SD = 0.67,979; t (285) = −2.242, p = .026, Cohen’s d = −0.314).

Next, we used a Pearson correlation to examine the relationship between resilience level and examination score; no significant correlation, r (479) = 0.045, p = .330, was detected. Because of this non-significant relationship, we were unable to proceed to the third step of testing the mediating influence of resilience between ACEs and examination scores.

Attentional Control, ACE Burden, and Examination Score

Univariate ANOVA was used to examine if attentional control varied as a function of ACE burden, as per our fifth hypothesis. Contrary to our predictions, no statistically significant difference was detected, F (2,396) =1.690, p = .186). Mean scores on the ACS obtained from our sample were comparable to those found in previous studies on undergraduates, including those reporting zero ACEs (M = 49.255, SD = 6.935), moderate ACEs (M = 47.821, SD = 8.412), and high ACEs (M = 49.250, SD = 7.589). Follow-up planned comparisons (t-tests) confirmed that there was no significant difference in attentional control between those with zero ACEs, and moderate ACEs, t (333) = 1.666, p = .097); between moderate ACEs and high ACEs, t (252) = −1.204, p = .230; and between zero ACEs and high ACEs, t (207) = −1.204, p = .230.

A Pearson correlation was used to examine if a relationship exists between attentional control (M = 48.58, SD = 7.74) and examination score (M = 74.35, SD = 15.61) across the sample. A positive relationship was found between attentional control and examination score, r (415) = .099, p = .044). Nevertheless, because the lack of relationship between ACEs and attentional control, we were unable to test our mediational model linking ACEs, attentional control, and examination score.

Discussion

The current study aimed to look at the cross-sectional relationships between ACE and examination score, accounting for the mitigating effects of resilience and attentional control. Consistent with prior studies, we confirmed that ACE endorsement was high in our sample, and was relatively higher within FG versus CG students. The

ACEs, Generational Status, and Impact on Exam Scores

ACE Burden

Regarding to our first hypothesis, 64% percent of students reported at least one ACE, with 48% reporting moderate (i.e., 1–3) ACEs, and 16% endorsing high ACEs. Our results are consistent with a recent review of 32 Canadian ACE studies, where half to two-thirds of participants reported at least one ACE (Carsley & Oei, 2020). That said, our estimates are higher than some other studies that estimate prevalence in the 52–54% range (Felitti et al., 1998; Karatekin, 2016; McDonald & Tough, 2013). Our higher estimate may be because of the modification we were required to make, removing the explicit “before 18 years” referent for our scale. One ACE in particular that may be more prevalent amongst college-aged adults – and may have inflated our estimates – is inappropriate sexual contact. For example, some studies specify that ACEs must occur prior to age 18 (Pasha-Zaidi et al., 2020), while others include age 18 (CDC, 2016; Felitti et al., 1998). Statistics Canada reports 71% of Canadian post-secondary students endorsed witnessing or experiencing at least one unwanted sexual advance. When reflecting on only 1 year of experiences connected to post-secondary education, 11% of women reported experiencing sexual assault (Burczycka, 2020). In our sample, 15.2% of the sample overall reported inappropriate sexual contact. As we did not use the “before 18” specifier, it is possible that some participants were responding to campus events, thereby elevating their ACE score. Another item that could have elevated our estimates is the fact that 26% of our participants endorsed the ACE “living with someone who is mentally ill and/or has attempted suicide”, which is substantially higher than previously reported rates in both undergraduate (16%, Windle et al., 2018) and adult samples (19%, CDC, 2014, as cited by Windle et al., 2018). University may seem like a safe haven to such students, a way to escape and experience stability and routine.

ACEs and Generational Status

Supporting our second hypothesis, FG students reported higher ACE exposure (both childhood maltreatment and household dysfunction) when compared to CG students. Future research should clarify further the meaning of this difference. For example, while this increased ACE exposure may be due to the negative effects of being from lower SES (OECD, 2018), unfortunately, we did not control for SES per se in the current study. Additionally, FG students are also more likely to be culturally diverse than CG students, leading to greater likelihood of different ACEs such as intergenerational trauma, discrimination, and racism (Azmitia et al., 2018), issues that may persist into emerging adulthood and beyond. As the definition of ACEs continues to be debated and refined, inclusion of these more systemic aspects of harm may prove illuminating, particularly when it comes to persons of lower SES.

Impact of ACEs on Exam Performance

Contrary to our third hypothesis, examination score did not differ between ACE burden categories. Academic success is multifaceted, and this suggests that looking at examination scores alone – and an examination early in the semester before stress has accumulated – may be too coarse of a measure of the impact of ACEs on academic success. Another explanation may be, as Arnett (2016) has argued, that emerging adults entering higher education may be higher functioning than their peers in the general population. That is, in order to achieve sufficient grades to compete for tertiary education, these individuals must have already found ways to offset the impact of ACEs on their functioning. That said, prolonged stress, either over the course of the semester or the course of a degree, may eventually take its toll on these at-risk students, and longer-term follow-up would be needed to see if such students can maintain their academic performance over time and if so, at what cost to their emotional well-being.

Investigating the Mitigating Impacts of Resilience and Attentional Control

The Impact of Resilience

Contrary to our fourth hypothesis, our results indicated that the zero and high ACEs groups had comparable resilience, with significantly lower resilience in the moderate ACE group. This is probably the most novel of our findings, and contradicts a recent meta-analysis demonstrating a linear relationship between ACEs and resilience across developmentally and culturally diverse samples of youth (Morgan et al., 2022). That said, upon further reflection, our findings make conceptual sense. Typically, gaining entrance to university requires the ability to overcome many obstacles, academic, financial, and socioemotional. It would make sense, then, that simply to arrive at university, the most ACE-exposed students would need to have a high level of resilience. This is consistent with Windle et al.’s (2018) findings that the amount of college students who have experienced a high frequency of ACEs, defined as four or more, is fairly widespread. The lack of significant difference in resilience between zero and high ACEs was unexpected, but there are two explanations. First, it is possible that persons with zero ACEs grew up in stable and securely attached environments that allowed for the development of a foundation of resilience (Darling Rasmussen et al., 2019). Second, the finding may pertain to how resilience was measured in this study. It is possible that individuals who have not experienced ACEs have advantages such as money, stability, health, and other effective supports in place that help to externally protect against stressors. Resilience is recognized as an internal dispositional trait, but it can be influenced externally (Pooley & Cohen, 2010), including through social support and economic privilege. Our study also suggests that the moderate ACE group may be the highest risk group in terms of resilience, and a potential target for early prevention-intervention to insure their continued academic success and retention.

Contrary to our hypotheses, we found no relationships between resilience and examination score. Perhaps with this being the first examination of semester, it did not solicit a high enough level of stress to require resilience activation. We might see more variation in the relationship between resilience and examination scores as the semester goes on, or even in subsequent years of the degree. As noted previously, examination scores are but one measure of academic success, so a more multifaceted approach to this construct in future studies would perhaps yield more positive findings.

The Impact of Attentional Control

In our fifth and final hypothesis we sought to understand whether ACEs did, in fact, have a negative impact on attentional control, and whether this, in turn, impacted examination scores. Contrary to our predictions (and those of prior literature), there was no relationship detected between ACE burden and attentional control. This may be explained by the fact that persons with high ACEs are capable of achieving the same level of attentional functioning, but have to work harder to do so (Taylor et al., 2019). This means that we may see different findings as the semester goes on. Conversely, the current study only reports on a self-report measure of attentional functioning; using an objective measure of attentional control may increase sensitivity of detecting attentional deficits and abilities (Ji & Wang, 2018). Finally, it is possible that the lack of relationship between these variables is a function of the population under study, i.e., undergraduates. That is, undergraduates are already by definition a higher functioning group in order to obtain the necessary grades to attain admission to university. It is possible that, were these variables examined in the population at large, there would be a discernible relationship between ACEs and attention control. We did, however, find that attentional control was positively related to examination score, as anticipated. Although the effect size was small, it appeared that higher levels of attention related to greater examination scores, which is unsurprising. This finding adds to a growing body of literature that attentional control aids in academic success and has potential implications for targeting effective supports for undergraduate students (Weyandt et al., 2013).

Practical Implications of the Findings

The current study has several important implications. First, our findings indicate that ACEs are an important area for continued study in the higher education context. While there were some null results in our study, this does not preclude such variables becoming significantly linked over time. ACEs are considered a risk factor for adverse adult outcomes. Under the diathesis-stress framework, a biological or social predisposition for mental health difficulties (namely, ACEs) interacts with intensified stress during the developmental period of emerging adulthood to increase overall risk for poor mental health outcomes (Pedrelli et al., 2015; Sheldon et al., 2021). Population-based primary-prevention programs to enhance resilience skills may help students maintain their functioning across their academic training as stress continues to accumulate (Van Genugten et al., 2017). Currently, it appears that the majority of programs designed to combat ACEs are primarily designed for prevention and mitigation of ACEs in children (Carsley & Oei, 2020). This suggests a potentially fruitful area for further exploration, designing trauma-informed programs to mitigate the impact of ACEs in emerging adult undergraduates (Brunzell et al., 2016). In particular, our study suggests that it may be the students with moderate ACEs – and not the highest ACEs – who may be the most vulnerable and could benefit most from primary-prevention program and resilience enhancement.

Our study also highlights the importance of differentiating between FG and/or low SES students compared to CG students. The former may be at greater risk for ACEs, yet even less likely to seek supports. Students from the working class and poverty may not share the same individualistic norms as predominate in the upper middle-class context of academia, and are more likely to be interdependent in their orientation (Stephens et al., 2012). They may be more susceptible to isolation, and may have even greater difficulty finding effective remedies in the academic context (Halfon et al., 2017; OECD, 2018; Pasha-Zaidi et al., 2020). FG students are also more likely to be culturally diverse in other ways, and subject to specific ACEs such as intergenerational trauma, discrimination, and racism (Azmitia et al., 2018). These prior studies – and our current findings – suggest that faculty may benefit from being aware of who their FG students are. Faculty being proactive about offering academic support can be a way of offsetting FG students’ particular stress vulnerability, as well as promoting inclusivity on campus for students who experience a cultural mismatch being from a lower SES background.

Limitations and Future Directions

While our study had several strengths, specific limitations must be noted that could be improved in future studies. The first limitation is that, because of practical reasons, we had to modify our ACE scale to remove the “before 18 years” referent. Although we tried to prime participants to regard our ACE items as “true” ACEs, it is possible that they considered them within their adult life. Future replications are needed with the 18 years referent to corroborate our findings. While a strength of our study was the robust sample size, representative of large Psychology classes at our institution, our sample was primarily female and Caucasian, which may impact the ability to generalize to all undergraduates, particularly those in more culturally and socioeconomically diverse locales. Additionally, our definition of FG focused on the participant being the first person in their family to attend higher education. We failed to stipulate that if a sibling of the participant had also attended post-secondary school, but not the parents or the grandparents, they would still be considered FG, which means that we may have underestimated the prevalence of FG participants in our sample. Our data collection was also limited by time constraints and participant burden, which meant certain variables could not be assessed or may be better assessed in future studies. For example, our measure of resilience was a brief, 6-item measure of perceived resilience. Future studies may benefit from including other measures of individual resilience, or other types, such as contextual resilience (Longhi et al., 2021). Moreover, looking at resilience early in the semester may be too premature, before sufficient stress has accumulated. Future studies would benefit from looking at different time-points in the semester, as well as concomitant measures of stress. Again, due to time-constraints, the current study only included self-report measures of our constructs of interest. Future studies would benefit from the addition of behavioral measures (e.g., computerized attention tasks) as well as broader metrics of academic success. In fact, longitudinal follow-up from first year may be particularly worthwhile. A study conducted by Otero (2021) determined that there was no difference between zero and one ACEs, but each additional ACE correlated with a 17% decrease in timely degree obtainment. As only 65.9% of students graduate from our institution, as per MacLean’s report (2018), a cross-sectional examination of students from different years in their undergraduate degree, or a longitudinal study over the 4-year term may prove beneficial.

Conclusion

Our study uncovered a high prevalence of students who have experienced ACEs, with even greater exposure for FG students. Resilience may serve as a buffer against the downstream effects of ACEs, and early efforts to foster student resilience could set them up for success and act as a stress buffer across the rigors of their educational journey.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the University of Victoria – Learning and Teaching Support and Innovation (Scholarship of Teaching and Learning mechanism; PI: C. Smart).

Open Practices

Due to privacy restrictions from our institutional ethics board, the raw data, analysis code, and materials used in this study are not openly available but are available upon request to the corresponding author. No aspects of the study were pre-registered.

ORCID iD

Colette M. Smart

Author Biographies

Sarah K. York, B.Sc., is a recent Graduate from the Undergraduate Honors Psychology program at the University of Victoria. he current manuscript reflects her final Honors thesis. Sarah has previously been involved in psychology research across the lifespan, from emerging adulthood to older adulthood. She is interested in resilience, well-being, and positive psychology approaches to enhance human flourishing, and hopes in the future to further her training in counselling and coaching methods.

Louise Chim, Ph.D., is an Associate Teaching Professor of Psychology at the University of Victoria. She specializes in undergraduate teaching including introductory psychology, statistics, cultural psychology, and psychology of diversity. She is interested in finding ways to facilitate student engagement and learning in large first-year courses (e.g., introductory psychology) and in courses that students may find daunting (e.g., statistics).

Jeremy Viczko, M.Sc., is a Doctoral Candidate in the Clinical Psychology program at the University of Victoria (Neuropsychology Focus). At the broadest level, his research interests are based in understanding the neuroscience of different states of consciousness from basic scientific and clinically applied perspectives. His research projects have spanned cognitive, behavioural and neural investigations into the effects of mindfulness meditation practices on attention and self-regulation, evaluating technology-integrated wellness training systems, and the role sleep in various memory consolidation processes. Jeremy takes a multimodal approach involving combinations of electroencephalographic (EEG) recordings, cognitive and behavioural tasks, as well as quantitative and qualitative questionnaire measures.

Patrick Nicoll, M.Sc., is a Doctoral Candidate in the Clinical Psychology program at the University of Victoria (Lifespan Focus). Patrick’s research interests center around resilience and transformational coping in the aftermath of trauma or otherwise extreme and challenging experiences. Specifically, his research focuses on understanding the cognitive, behavioural, and emotional mechanisms that catalyze healthy responses to adversity, particularly in research subjects who have been exposed to isolated, confined, and extreme (ICE) environments.

Cheiyenne Fontanilla, B.Sc. (Hons), is a recent Graduate from the Undergraduate Honors Psychology program at the University of Victoria. She has been involved in research spanning the broad areas of clinical neuropsychology and lifespan psychology throughout her undergraduate career. Her general research interests include cultural and community psychology, decolonization and its methods, and development across the lifespan. Her current research examines the early developmental stages of colonial mentality, a form of internalized oppression, among Filipinx Canadian youth. Cheiyenne is currently working as an intern with the Department of Justice at the Government of the Northwest Territories and plans to pursue a doctoral-level education in Clinical Psychology.

Andrea Kasian, B.A. (Hons) is a recent Graduate from the University of Victoria’s Honors Psychology program. Her coursework specialized in child and youth care which led her to complete a thesis evaluating the implementation of educational and cognitive interventions for children with autism spectrum disorder. Her involvement in research currently is widespread utilizing both quantitative and qualitative measures on projects analyzing cognitive rehabilitation methods, mindfulness meditation, trauma and stress, neurodevelopmental disorders, executive functioning and self-regulation. Her future endeavors include her aspiration to receive further education to become a registered clinical counsellor for child and adolescent populations.

Colette M. Smart, Ph.D., is an Associate Professor of Psychology at the University of Victoria and a Registered Psychologist. Her research program broadly focuses on self-regulation as a transdiagnostic factor across persons dealing with trauma as well as neurological disorders such as acquired brain injury. She is also interested in the development of self-regulation, particularly during the sensitive emerging adulthood period, and how training in self-regulation training can be used to enhance resilience. Clinically, her expertise is in the areas of clinical neuropsychology and cognitive rehabilitation. She maintains a private practice providing interventions to adults with acquired brain injury and neurological disorders. She enjoys leveraging these clinical experiences and knowledge to enhance her classroom teaching and supervision.

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