Strengths Associated With Positive Youth Development After Child Maltreatment: Application of the Resilience Portfolio Model

Child Maltreatment and Adolescent Prosocial Skills and Academic Achievement

Child maltreatment, such as physical, emotional, and sexual abuse, as well as neglect, has been linked to impairment in youth social development (Chen et al., 2023). Studies have shown that adolescents who experience maltreatment are more likely to display lower levels of empathy, cooperation, and perspective-taking, which are central to prosocial behavior (Chen et al., 2023; Cigala & Mori, 2022). Child maltreatment may impair fundamental developmental processes of social skills (e.g., empathic concern) and peer integration, leading to disrupted social and prosocial functioning (Chen et al., 2023; Kamis & Copeland, 2024).

Additionally, child maltreatment has been associated with poorer academic outcomes, with effects spanning attention, problem-solving, and school engagement (McGuire & Jackson, 2018; Romano et al., 2015; Zhang et al., 2025). Research shows that children who experience maltreatment are at increased risk for lower standardized test scores, higher rates of grade repetition, and more frequent disciplinary problems, and overall lower grade point averages (McGuire & Jackson, 2018; Romano et al., 2015; Zhang et al., 2025). Child neglect reduces youths’ cognitive and social stimulation, affecting the development of executive functioning and attention regulation (Perevoznikova et al., 2025; Sheridan & McLaughlin, 2014). These factors, in turn, predict poorer reading, mathematics, and problem-solving outcomes (Zhang et al., 2025).

Conceptual Framework

Although prior research shows an elevated risk of negative outcomes faced by youth who have experienced child maltreatment, studies of resilience show that various strengths and protective factors help youth overcome trauma and achieve positive outcomes (Meng et al., 2018; Yoon et al., 2021). In the resilience literature, scholars have emphasized the need for understanding how strengths contribute to child well-being through their cumulative presence and through their specific configuration (i.e., a portfolio of protective factors).

One important perspective in the resilience literature is a type-oriented approach that emphasizes the nature or configuration of strengths. From this perspective, not all strengths or protective factors may have the same effects. Building on this perspective, the resilience process may be best understood by considering the type, nature, and context of strengths rather than a simple accumulation. Thus, it is important to identify the most salient strengths and explore specific configurations of strengths that work synergistically to promote positive youth functioning (Hamby, Taylor, et al., 2018). This view aligns with the Resilience Portfolio Model (RPM; Hamby, Grych, et al., 2018), which posits that a set (or portfolio) of diverse assets and resources available to youth shapes the resilience process in adolescents, following exposure to violence and/or other adversity. The RPM categorizes strengths into four domains: regulatory, meaning-making, interpersonal, and environmental strengths (Banyard et al., 2025). Regulatory strengths include skills related to coping, self-regulation, and goal attainment. Interpersonal strengths encompass positive interpersonal relationships (e.g., social support) as well as internal assets to build and sustain such interpersonal connections (e.g., empathy, communication skills). Meaning-making strengths encompass the capacity to find meaning in challenging situations and include examples like a sense of purpose, positive future orientation, optimism, and hope. Environmental strengths include aspects of the natural (e.g., green spaces) and built (e.g., safe schools and neighborhoods) environments.

Another aspect of capturing the breadth of resilience portfolios within the RPM framework involves a dosage concept called poly-strengths (Hamby, 2025). The poly-strengths perspective (Hamby, Grych et al., 2018) posits that individuals with a greater number of different types of protective factors (i.e., “poly-strengths”) have better outcomes, such as better well-being and healthier functioning. This concept is parallel to the poly-victimization framework (Finkelhor et al., 2007) that highlights the cumulative burden of youth exposure to multiple forms of violence by multiple perpetrators in multiple settings. Mirroring the cumulative burden of poly-victimization, the poly-strengths framework suggests the cumulative protection powered by more resources and assets available to youth. It is related to another dosage concept, positive childhood experiences (Bethell et al., 2019), but poly-strengths focuses on a wider range of strengths and ones that are potentially malleable across the lifespan.

The concepts of resilience portfolios and poly-strengths offer significant guidance in examining the role of protective factors in building resilience among youth with a history of childhood maltreatment. From a type-oriented perspective, there is value in understanding resilience through specific types or constellations of strengths. From the cumulative and dosage of strengths perspective, there is an additive effect from each additional strength. In this view, the resilience process could be facilitated by increasing the density or quantity of strengths available to adolescents. Building on these conceptual notions, this study examines both the cumulative/dosage effects of poly-strengths and the role of specific portfolios of strengths in shaping social and academic functioning among youth exposed to child maltreatment.

Empirical Evidence on the Portfolios of Strengths

Empirical research has identified multidimensional, socioecological protective factors that map onto the four domains (regulatory, interpersonal, meaning-making, and environmental) of the RPM (Banyard et al., 2025; Hamby, Grych et al., 2018). In line with the RPM’s regulatory strengths domain, research has suggested that adolescents’ self-regulation skills, such as goal-setting and impulse control abilities, are associated with resilience (Dias & Cadime, 2017). Further, emotion regulation and positive coping strategies, such as adaptive coping (e.g., problem-focused coping, positive reframing), have been associated with positive outcomes following child maltreatment (Kim & Cicchetti, 2010; Zamir, 2022).

In addition to regulatory strengths, interpersonal strengths have been repeatedly shown to serve as key protective factors associated with positive social and academic outcomes among adolescents, following exposure to child maltreatment (Meng et al., 2018). Research has found that adolescents at risk for maltreatment still manage to adapt and thrive when positive, supportive relationships are present (Ratliff et al., 2023). Warm, responsive caregiving and secure attachment relationships promote emotion regulation, empathy, and prosocial behavior, thereby buffering the social consequences of maltreatment (Holmes et al., 2018; Yoon et al., 2023). In school settings, supportive peer relationships and teacher-student relationships have been associated with better outcomes, including reading competence and fewer externalizing symptoms (Jean-Thorn et al., 2023; Meng et al., 2018). Collectively, these studies illustrate that protective relationships with caregivers, peers, and teachers are essential to resilient social and academic development among youth who have been exposed to child maltreatment.

Meaning-making strengths, including a sense of purpose, hope, spirituality, and future orientation, have also been highlighted as protective factors linked with positive youth outcomes after adversity (Howell & Miller-Graff, 2014; Yoon et al., 2025). For example, some forms of religious and spiritual engagements are associated with stronger coping and prosocial orientations in contexts of violence exposure (Howell & Miller-Graff, 2014). Furthermore, longitudinal research similarly suggests that aspirations and a future orientation help adolescents to maintain positive developmental pathways, even after early challenges (Oshri et al., 2018).

Finally, broader environmental factors, such as community contexts, also contribute to resilience among youth with a history of maltreatment (Jean-Thorn et al., 2023). Safe neighborhood environments have been identified as a critical protective factor linked to positive youth outcomes. For example, one study found that children who lived in relatively low-crime neighborhoods were more likely to display resilience after child maltreatment (Jaffee et al., 2007). Similarly, a study that focused on 771 adolescents involved with the child welfare system found that neighborhood safety was associated with stable and continued resilient functioning, operationalized as competencies in behavioral, emotional, social, and cognitive domains of functioning, over time (Yoon et al., 2021).

Empirical Evidence on the Cumulative Impact of Strengths

Research has suggested that protective factors build upon one another, and the more support youth have, the better their adjustment tends to be (Masten, 2018). Though relatively limited research has specifically examined the cumulative effects of strengths or protective factors on youth academic and social functioning in the context of child maltreatment, the broad literature on poly-strengths has consistently suggested that poly-strengths are significantly associated with positive outcomes in the context of adversities (Brooks et al., 2024; Hamby et al., 2020a, 2025, 2020b). A systematic review of 119 studies found that protective influences across individual, family, peer, school, and community dimensions can help build academic resilience, with evidence suggesting additive benefits when multiple assets co-occur (Ye et al., 2024). Another systematic review similarly indicated the benefit of considering co-occurring multiple protective factors, versus a single protective factor, that collectively promote better mental health resilience (Fritz et al., 2018).

The cumulative impact of protective factors is also well-documented in empirical studies examining the effects of positive childhood experiences (PCEs) on individuals’ health and well-being. PCEs refer to favorable and meaningful experiences during their first 18 years of life, such as having a good friend, maintaining predictable home routines, and experiencing safe and stable relationships and environments (Narayan et al., 2018; Sege et al., 2025). Prior studies have shown that a greater number of PCEs is associated with more favorable outcomes, such as better mental health, stronger academic skills, and greater social-emotional support, highlighting the additive effects of PCEs (Bethell et al., 2019; Guo et al., 2022; Narayan et al., 2018; Sege et al., 2025). This study builds on and extends the PCE literature by examining the cumulative effects of a multidimensional set of strengths across the four RPM domains (regulatory, meaning-making, interpersonal, and environmental), thereby broadening the conceptualization of protective factors beyond the predominantly interpersonal focus of prior PCE work (e.g., Bethell et al., 2019; Sege et al., 2025).

The Current Study

Guided by the RPM, the current study aimed to investigate how unique combinations of strengths and their cumulative presence (i.e., poly-strengths) predict adolescent outcomes. The RPM framework and the dosage-based approaches (e.g., poly-strengths indices, PCE approach) have documented resilience portfolios and the cumulative protective effects of strengths (e.g., PCEs) among youth and adults in the general population (Guo et al., 2022; Hamby et al., 2020a, 2020b; Sege et al., 2025). However, these approaches have not yet been widely applied to young adolescents involved in the U.S. child welfare system, a population exposed to high levels of adversity and often experiencing complex contextual experiences that may influence their developmental outcomes. For example, factors such as out-of-home care placement and placement instability have been shown to shape youths’ socioemotional functioning and academic outcomes (Gypen et al., 2017; Maguire et al., 2024).

The current study extends the literature by applying the RPM framework to adolescents involved with the U.S. child welfare system via two complementary approaches: the configuration of strengths and the cumulative effects of strengths. Specifically, we focused on two research questions. First, how are distinct portfolios of strengths associated with prosocial skills and academic achievement among adolescents with a history of child maltreatment? Second, how is the poly-strengths index associated with prosocial skills and academic outcomes? It was hypothesized that more comprehensive portfolios of strengths would be associated with higher levels of prosocial skills and better academic outcomes. Additionally, it was hypothesized that a greater number of strengths would be associated with higher levels of prosocial skills and better academic outcomes.

Data and Sample

Data are drawn from the second cohort of the National Survey of Children and Adolescent Well-Being II (NSCAW II), which is a nationally representative sample of 5,872 youths aged between 0-17.5 years who were investigated by Child Protective Services (CPS) for child maltreatment between February 2008 and May 2009. NSCAW II recruited families using a two-stage stratified sampling design from 81 Primary Sampling Units (PSUs) representing child welfare agencies across 30 states in the United States. Data were collected through face-to-face interviews or assessments with children, their caregivers, their child welfare caseworkers, and their teachers between April 2008 and January 2011. Data were collected at three time points: (1) T1 (baseline; about 2-6 months after the close of CPS investigation), (2) T2: approximately 18 months after the completion of the baseline assessment, and (3) T3: approximately 36 months after the baseline assessment. The current study used data collected at T1 and T2. T3 data were not included in the present analyses due to substantially higher levels of missingness. By the 36-month follow-up, many youths (n = 159) were missing outcome data due to study attrition and/or having reached age 18, when outcome data were no longer collected. Including T3 outcomes data would have substantially reduced the analytic sample. We limited our sample to 1,054 adolescents who were at least 11 years old at T1, because child self-report data on the key study variables were only available from adolescents aged 11 and older. Our sample was further limited to those who had complete data on all variables used in the present study, resulting in a final analytic sample of 357 adolescents. A sample comparison analysis indicated that adolescents excluded from the study were slightly older (M = 13.98) and were more likely to be males (46.92%) compared to those in the analytic sample (M = 13.21; males = 40.34%). There were no other statistically significant differences between the two groups.

Regarding sex, 40.34% of the participants identified as male. Adolescents averaged 13.21 years of age (SD = 1.61). Regarding race/ethnicity, the largest proportions of participants identified as non-Hispanic White (39.78%) or non-Hispanic Black (27.45%), followed by Hispanic (any race) (23.25%), and other racial/ethnic groups (9.52%). Nearly seven in ten caregivers (68.35%) had completed high school or pursued higher education, and approximately 45% of the analytic sample lived in families with incomes below 100% of the federal poverty level. 22.97% were placed in out-of-home care, with the average number of placement changes being 1.39. Table 1 summarizes the descriptive statistics for key study variables.

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Table 1.

Demographic Characteristics & Descriptive Statistics for Key Study Variables (N = 357)

​	%	M (SD)	Range
Sociodemographic characteristics (covariates)
Child age	​	13.21 (1.61)	11 – 16
Sex (male)	40.34	​	​
Child race
White	39.78	​	​
Black	27.45	​	​
Hispanic	23.25	​	​
Other	9.52	​	​
Caregiver’s education (HS or higher)	68.35	​	​
Poverty (below 100% FPL)	44.82	​	​
Out-of-home placement	22.97	​	​
# of placements	​	1.39 (0.86)	1 – 6
Dimensions of resilience
Meaning-making strengths	​	​	​
Religious beliefs	50.42	​	​
Future expectations	61.62	​	​
Regulatory strengths
Subclinical anxiety/depression scores	87.96	​	​
Subclinical aggression scores	73.67	​	​
Interpersonal strengths
Teacher relationships	49.58	​	​
Peer relationships	45.10	​	​
Caregiver-child relationship	29.97	​	​
Environmental strengths
Community safety	45.10	​	​
Distal child developmental outcomes
Prosocial skills	​	94.77 (15.35)	52 – 130
Applied problems	​	87.25 (13.81)	32 – 123
Letter-word identification	​	92.59 (18.41)	6 – 137

Measures

Data Analysis

To identify distinct strengths portfolios in child welfare-involved youth, we employed latent class analysis (LCA). LCA is a person-centered analytic approach that identifies unobserved subgroups (i.e., latent classes) based on individuals’ shared characteristics and response patterns across multiple observed indicators (Collins & Lanza, 2009). LCA is well-suited for this study because it allows for the empirical identification of unique configurations of strengths (i.e., resilience portfolios) across domains rather than examining individual factors in isolation. For the present study, we followed the manual three-step approach to LCA: (1) identifying the optimal number of latent classes through a series of unconditional models (i.e., models without covariates or outcomes), (2) assigning individuals to their most likely class based on posterior probabilities, and (3) fitting auxiliary models with covariates (Asparouhov & Muthén, 2014; Nylund-Gibson et al., 2019).

In Step 1, a series of unconditional LCA models were analyzed by incrementally increasing the number of latent classes from 2 to 5. The best-fitting unconditional LCA model was identified using multiple fit indices and criteria (Collins & Lanza, 2009). The AIC, BIC, and sample-size-adjusted BIC assess model fit, with lower values indicating better fit (Nylund-Gibson & Choi, 2018). Entropy quantifies the precision of class assignment, where values near 1.0 suggest strong separation between classes and low classification error. The Lo-Mendell-Rubin Likelihood Ratio Test (LMR-LRT) and Bootstrap Likelihood Ratio Test (BLRT) compare models with k and k - 1 classes, with significant p-values supporting the k-class model (Nylund-Gibson & Choi, 2018). Additionally, we considered class proportions (avoiding classes with <5% of the sample) (Hipp & Bauer, 2006) and the theoretical interpretability of the resulting classes. In Step 2, we computed the estimated conditional probabilities for modal class assignment to account for classification uncertainty. Step 3 involved fitting the auxiliary model with covariates. Covariates and distal outcomes were incorporated into the model to examine relationships between the identified latent classes and covariates and to assess pairwise mean differences in distal outcomes across latent classes while accounting for classification errors. Missing data was handled using full information maximum likelihood (FIML) estimation. All LCA analyses were performed using Mplus Version 8.0 (Muthén & Muthén, 1998).

To examine the cumulative effects of strengths on youth development, we employed a variable-centered analysis examining the association between cumulative strengths and youth developmental outcomes (i.e., prosocial skills and school achievement) using the same analytic sample. We conducted a series of Ordinary Least Squares (OLS) multiple regression analyses, regressing child developmental outcomes on the strengths index, while controlling for the same set of covariates used in LCA analyses. We evaluated multicollinearity by examining the Variance Inflation Factor (VIF) values, all of which fell below 2.5, indicating no evidence of problematic multicollinearity (Allison, 1999).

Portfolios of Strengths

Fit indices for the unconditional LCA models ranging from 2- to 5-class solutions are presented in Table 2. Model fit indices provided mixed guidance regarding the optimal number of classes. AIC decreased with each additional class, and ABIC reached its lowest value at the 3-class solution. In contrast, BIC favored the most parsimonious 2-class solution. Although VLMR-LRT was not statistically significant for the 3-class solution, the BLRT remained significant for the 3-class solution. BLRT test was non-significant for the 4-class model, suggesting that adding a fourth class did not result in a meaningful improvement in model fit. Entropy values exceeded the recommended threshold of 0.70 (Nagin, 2005) for the 3-class model, suggesting acceptable classification precision. Taking into account the BLRT, AIC, ABIC, entropy, and the substantive interpretability and theoretical coherence of the latent classes, the 3-class model was selected as the best-fitting solution.

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Table 2.

Fit Indices for Selecting Unconditional Model (N = 357)

​	2-class	3-class	4-class	5-class
AIC	3462.344	3444.830	3443.037	3438.917
BIC	3528.265	3545.651	3578.758	3609.537
ABIC	3474.333	3463.167	3467.721	3469.949
Entropy	0.582	0.772	0.831	0.800
VLMR-LRT	<.001	0.049	0.079	0.0488
BLRT	<.001	<.001	0.1667	0.0667
% of class 1	48.94	60.28	4.11	16.73
% of class 2	51.07	22.80	26.35	22.86
% of class 3	​	16.92	58.18	4.12
% of class 4	​	​	11.36	4.49
% of class 5	​	​	​	51.80

The 3-class solution is presented in Figure 1. Class labels were assigned based on distinctive patterns across the RPM resilience domains. The Future-oriented but low in aggression control class (Class 1) represented the smallest group, comprising 16.9% of participants. This class was characterized by relatively high levels of future expectations but showed low levels of emotion regulation for aggression. The Multi-domain resilience portfolios class (Class 2) constituted the largest subgroup at 60.3% of the sample. This class was distinguished by consistently elevated strengths across meaning-making, regulatory, interpersonal, and environmental domains. Particularly, adolescents in this class were above the other two classes in the regulatory, interpersonal, and environmental domains. Finally, the Well-regulated but limited meaning and connection class (Class 3; 22.8%) was characterized by relatively higher levels of emotion regulation, particularly in managing internalizing symptoms, but exhibited lower levels of meaning-making and interpersonal strengths.

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Figure 1.

The 3-class LCA model: Strengths portfolios

Associations of Class Membership With Youth Developmental Outcomes

Table 3 presents the means of the distal outcomes for each class and their pairwise mean differences for the three youth developmental outcomes between the three latent classes. The values in the last two columns indicate the mean differences between the mean of the profile in the row and the mean of the profile in the column. For prosocial skills, the Multi-domain resilience portfolios class had significantly higher prosocial skills (distal mean = 95.49) compared to the Well-regulated but limited meaning and connection class (distal mean = 88.24). For applied problems, the Future-oriented but low in aggression control class (distal mean = 100.87) and the Multi-domain resilience portfolios class (distal mean = 100.05) had significantly higher mean scores of applied problems compared to the Well-regulated but limited meaning and connection class (distal mean = 82.07). For letter-word identification, similar to applied problems, the Future-oriented but low in aggression control class (distal mean = 111.47) and the Multi-domain resilience portfolios class (distal mean = 108.91) had significantly higher mean scores of letter-word identification compared to the Well-regulated but limited meaning and connection class (distal mean = 92.84).

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Table 3.

Distal Mean Differences Between Four Latent Classes (N = 357)

Distal outcome	Class	Distal mean	Future-oriented but low in aggression control	Multi-domain resilience portfolios
Prosocial skills	Future-oriented but low in aggression control	90.879	​	​
	Multi-domain resilience portfolios	95.494	−4.615	​
	Well-regulated but limited meaning and connection	88.243	2.636	7.251*
Applied problems	Future-oriented but low in aggression control	100.868	​	​
	Multi-domain resilience portfolios	100.050	0.818	​
	Well-regulated but limited meaning and connection	82.074	18.794**	17.975**
Letter-word identification	Future-oriented but low in aggression control	111.470	​	​
	Multi-domain resilience portfolios	108.907	2.564	​
	Well-regulated but limited meaning and connection	92.844	18.626**	16.062*

Poly-Strengths: Cumulative Impact of Strengths on Youth Developmental Outcomes

Results of the OLS regression analyses examining the associations between the poly-strengths index and child developmental outcomes (Table 4) showed that a higher level of poly-strengths was significantly associated with greater prosocial skills (B = 1.65, p < .001). No significant associations emerged between the poly-strengths index and either of the school achievement measures (i.e., applied problems, letter-word identification). Among covariates, caregivers’ education level was positively associated with greater prosocial skills (B = 4.52, p = 0.014) and higher applied problems scores (B = 3.74, p = 0.025). In addition, older child age was significantly associated with lower letter-word identification scores (B = −1.57, p = 0.013).

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Table 4.

Associations Between the Poly-Strengths Index and Child Developmental Outcomes (N = 357)

​	Prosocial skills			Applied problems			Letter-word identification
	B	SE	p	B	SE	p	B	SE	p
Poly-strengths index	1.65	0.46	<.001	0.54	0.42	0.201	−0.19	0.56	0.739
Child age	−0.32	0.51	0.539	−0.82	0.47	0.080	−1.57	0.63	0.013
Child sex (male)	1.07	1.69	0.527	2.53	1.54	0.101	−1.30	2.06	0.528
Child race/ethnicity
Non-Hispanic Black	2.33	2.04	0.255	−1.39	1.86	0.453	−2.70	2.49	0.279
Hispanic	1.92	2.14	0.371	−0.46	1.95	0.813	0.08	2.62	0.974
Other	3.63	2.88	0.208	−0.61	2.62	0.816	1.34	3.52	0.703
Caregiver education level (HS or higher)	4.52	1.82	0.014	3.74	1.66	0.025	3.65	2.23	0.102
Caregiver poverty (≤100% FPL)	0.50	1.71	0.769	−1.93	1.56	0.215	−1.96	2.08	0.349
Child maltreatment type
Sexual abuse	−0.75	2.23	0.736	−1.47	2.10	0.485	−0.81	2.82	0.773
Emotional abuse	3.68	3.11	0.238	1.29	2.96	0.662	1.44	3.96	0.717
Neglect	2.41	1.84	0.192	−1.21	1.72	0.484	−1.17	2.31	0.614
Out-of-home placement	0.72	2.01	0.721	−0.69	1.83	0.705	−1.10	2.45	0.655
# of placements	−1.07	0.98	0.275	−0.34	0.89	0.700	1.82	1.19	0.128

Limitations

The findings should be interpreted in light of study limitations. Although we sought to establish temporal ordering of the study variables by using T1 strengths and T2 youth outcomes, causal inferences between study variables cannot be made due to the cross-sectional nature of the data used in the study. Future research could use longitudinal data with repeated measures to examine how strengths portfolios evolve and contribute to developmental trajectories of resilience over time. Additionally, limitations in study measures, particularly strengths indicators, should be acknowledged. We were unable to include precise indicators of regulatory and environmental strengths due to limited data availability in the NSCAW-II dataset. For instance, lack of aggression and anxiety/depression was used as a proxy for behavior management in this study, rather than direct measures of positive regulatory capacities, such as positive emotion regulation and psychological endurance (Hamby et al., 2025). Future studies should include more comprehensive and accurate assessments of strengths across the regulatory, meaning-making, interpersonal, and environmental domains. Similarly, data on potentially important contextual factors, such as youths’ access to trauma-focused services or families’ engagement with other support systems between T1 and T2, were not available and therefore could not be accounted for in the present study. Future research should examine how these contextual factors may influence developmental outcomes among youth involved in the child welfare system. Additionally, the data used in this study were collected between 2008 and 2011; therefore, the findings may not fully reflect the strengths and resources available to youth today, including more recent developments such as digital tools and online supports. Finally, our sample consisted of adolescents formally involved in the child welfare system, and thus, the study findings may not be generalizable to other populations.

Implications

The present findings underscore a critical need for strengths-based interventions specifically designed to broaden and build upon the resilience of youth who have experienced child maltreatment. The use of LCA, a person-centered analytic approach, allowed the identification of heterogeneous portfolios of strengths and highlighted the value of personalized intervention strategies for this population. The three distinct resilience portfolios identified in this study suggest that individualized, tailored approaches may be well-suited, and ultimately more effective, in addressing the multifaceted needs of this population. For instance, youth who demonstrate strong future orientation yet exhibit difficulties in managing behaviors may benefit most from targeted interventions focused on behavioral management. Such interventions could be further optimized by leveraging these youths’ existing goal-oriented traits to support and enhance their prosocial functioning. Moreover, given the central importance of meaning-making and interpersonal strengths for academic success, it seems essential to support youth with maltreatment histories in cultivating a belief in their capabilities, a future-oriented mindset, and positive peer and teacher relationships in this population. Strengthening these capacities may provide a promising pathway to improved educational outcomes and long-term well-being in this vulnerable population.

Conclusion

Informed by the RPM, this study integrated two interrelated, complementary conceptual notions in resilience research: the type/pattern-based approach, which identifies distinct configurations of strengths, and the cumulative approach, which emphasizes the total accumulation/dosage of strengths. The results showed that the Multi-domain resilience portfolios group consistently exhibited favorable outcomes across both prosocial and academic outcomes. Interestingly, children in the meaning-making primary group (i.e., Future-oriented but low in aggression control) showed significantly higher academic achievement compared to those in the regulatory primary group (i.e., Well-regulated but limited meaning and connection). This finding suggests that school-based interventions may benefit from prioritizing the cultivation of motivation and future-oriented thinking, rather than placing disproportionate emphasis on behavioral regulation and management alone. Findings from the poly-strengths approach indicated that although the overall number of strengths (i.e., poly-strengths index) predicted greater prosocial skills, it did not significantly predict academic achievement. Overall, the findings provide empirical support for conceptual models considering both the cumulative and configurational nature of strengths in resilience processes.