Reframing school-based restorative justice as a structural population health intervention

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Health and Place journal homepage: http://www.elsevier.com/locate/healthplace

Reframing school-based restorative justice as a structural population health intervention �lez c Jelena Todi�c a, *, Catherine Cubbin b, Marilyn Armour b, Michele Rountree b, Thalia Gonza a

The University of Texas at San Antonio, College of Public Policy, Department of Social Work, United States The University of Texas at Austin, Steve Hicks School of Social Work, United States c Occidental College, Politics Department, United States b

A R T I C L E I N F O

A B S T R A C T

Keywords: Restorative justice Structural interventions Schools Social determinants of health Health equity Health promotion

School-based restorative justice has gained national prominence as an effective approach to interrupting the school-to-prison pipeline. Remarkably, despite its simultaneous positive association with academic success, school safety, and school connectedness, most scholars and practitioners do not associate restorative justice with health. Using ecosocial theory, we conceptualize school-based restorative justice as a structural population health intervention. Our findings indicate that students attending schools using restorative justice have lower odds of missing school due to adverse health and better academic outcomes compared to students who do not. Restorative justice shows promise as a structural intervention that can contribute to improving population health.

K-12 schools are social structures with implications for students’ short- and long-term health outcomes (APHA, 2018; WHO, 2017; Lew­ allen et al., 2015); however, the biomedical and lifestyle perspecti­ ves—which emphasize the role of individual behaviors and minimize the effects of social conditions influencing health (Krieger, 2011)— continue to dominate approaches to school-based health interventions. The result are primarily interventions that focus on delivering school-based health care or changing individual behaviors (e.g., healthy eating or exercise) rather than seeking to transform the entire school structure into an equitable health-promoting place. Ongoing complex interactions between individual biology, school institutional dynamics, the individual and collective histories of students and staff, societal ar­ rangements of power, and ecological context interact and eventually “get under the skin" (Krieger, 2011). It is, therefore, critical to under­ stand schools as sites of embodiment and as structural determinants of health. While providing healthcare services and encouraging behavior change in K-12 schools is important, it is equally important to acknowledge that these approaches are limited in their ability to pro­ duce sustained improvements in reducing health inequities because they do not address the risk factors associated with social and environmental contexts that inhibit individuals’ and communities’ ability to attain the highest level of health (Shackleton, Jamal, Viner, Dickson, Patton and

Bonell, 2016; Brown et al., 2019; Shackleton, Jamal, Viner, Dickson, Hinds, K., Patton and Bonell, 2016). A growing body of evidence in­ dicates that multilevel structural interventions, which promote health by altering the structural context within which health is produced and reproduced, are needed to improve population health (Blankenship et al., 2006; Brown et al., 2019; Shackleton, Jamal, Viner, Dickson, Patton and Bonell, 2016). While school structures contain complex embodiment pathways that span from the U.S. political economy to individual student biology across space and time (Todic, Cubbin & Armour, 2019, Fig. 1), in this paper we focus on schools’ approach to discipline as a determinant of health. More specifically, we explore how this aspect of K-12 schools may reinforce or undo the historically grounded pathways of embodi­ ment rooted in the intersection of structural oppression based on race, gender, ability, and age. We propose that school-based restorative justice—“a whole school relational approach to building school climate and addressing student behavior through fostering belonging over exclusion, social engagement over control, and meaningful account­ ability over punishment” (Armour, 2014a)—is a structural intervention with positive implications for populations’ health. We hypothesize that students attending schools, which implement school-based restorative justice have better health and academic outcomes compared to students

* Corresponding author. The University of Texas at San Antonio, College for Health, Community and Policy, Frio Street Building (FS) 4.406 501 W, Cesar E. Chavez Blvd, San Antonio, 78207, TX, United States. E-mail address: [email protected] (J. Todi�c). https://doi.org/10.1016/j.healthplace.2020.102289 Received 23 October 2019; Accepted 23 January 2020 1353-8292/© 2020 Published by Elsevier Ltd.

Please cite this article as: Jelena Todić, Health & Place, https://doi.org/10.1016/j.healthplace.2020.102289

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punitive discipline begins as early as preschool, with Black children being the most likely to get suspended (U.S. Department of Education Office of Civil Rights, 2016). Assuming the cumulative life course perspective, harsh discipline school environment ultimately contributes to producing and reproducing inequitable health outcomes. The recur­ ring experiences with social inequity and stressful environments such as schools, accumulate over a person’s life span, increasing allostatic load (McEwen, 2012) and contributing to health decline associated with “weathering phenomenon” (Forde et al., 2019; Geronimus, 1992). Harsh school discipline is also associated with the higher prevalence of un­ healthy behaviors. Students who experience punitive discipline are more likely to engage in fighting, carrying a weapon, smoking, using alcohol and drugs, and participating in other health-harming acts (Fabelo et al., 2011; Lasnover, 2015; Skiba et al., 2016). Finally, the exclusionary punishment often sets off a chain of events leading to disengagement, percieved misbehavior, and academic failure, increasing the probability of school dropout (Advancement Project, 2014; 2015a; Balfanz et al., 2015; Chu and Ready, 2018; Noltem, Ward and Mcloughlin, 2015; Rausch and Skiba, 2017) and ultimately poor health (Lansford et al., 2016; Zajacova and Everett, 2014; Zajacova and Lawrence, 2018). Consequently, the American Psychological Associa­ tion (2008) and the American Academy of Pediatrics (2013) issued policy statements warning about the ineffectiveness of zero-tolerance policies and highlighting the harm of such policies to students, fam­ ilies, and communities.

who do not. Furthermore, we hypothesize that restorative justice mod­ erates the relationship between individual predictors—self-reported ethnicity and gender as they are associated with the disproportionate impact of exclusionary discipline–and these outcomes, especially benefiting boys and African American, American Indian, and Latino students because they bear the disproportionate impact of punitive school discipline. 1. Harsh school discipline as a pathway of embodiment Since the early 1990s, uncompromising punishment and reliance on exclusionary school disciplinary practices like suspensions and expul­ sions—also known as zero-tolerance policies—have dominated the approach to school discipline across the US (Curran, 2017; Fabelo et al., 2011; Skiba et al., 2016). While the Gun-Free Schools Act (GFSA) of 1994 mandated schools to expel students who bring a firearm to school for at least one year and refer them to the juvenile justice system (Curtis, 2014; Martinez, 2009), most communities across the country voluntarily expanded it to also include mandated harsh punishment (e.g., expul­ sions, suspensions, ticketing) for non-weapon activities such as assaults, drugs, or minor offenses (e.g., “insubordination,” language, possession of cell phone, or public displays of affection; Advancement Project, 2010; Curran, 2017). While the central assumptions behind zero-tolerance policies included fairness and consistency through pre­ determined consequences (American Psychological Association [APA], 2008), the policies criminalized youth behaviors that schools handled internally in the past (Fowler et al., 2010). This uniform approach to school discipline also assumed that such interventions would be inher­ ently fair, but are often not, as numerous studies on bias in discipline practices document (Carter et al., 2017; Girvan et al., 2017; Gregory and Roberts, 2017). This shift in school discipline policies may impact health through multiple plausible embodiment pathways. Contrary to their intention, higher rates of suspensions and expulsions decrease feelings of safety and lead to less satisfactory ratings of school climate (Skiba et al., 2014; American Psychological Association, 2008). Moreover, while punitive school policies negatively impact all children, the impact is not evenly distributed among all student groups. Youth of color do not misbehave more than their White peers (Anyon et al., 2014; Bradshaw, Mitchell, O’Brennan and Leaf, 2010; Payne and Welch, 2010); yet, race remains a significant predictor of out-of-school suspension regardless of the severity of behavior (Skiba et al., 2016; Skiba et al., 2011). Black boys bear most of the harsh school discipline burden, but they are not alone. Evidence suggests that Black girls, American Indian, Latino, and LGBT students, and students with disabilities, also have higher odds of being punished more harshly compared to their peers (Morris and Perry, 2017; Himmelstein and Brückner, 2011). Even more alarming is that the use of

2. Restorative justice and pathways of embodiment Rooted in the traditional practices of the indigenous people of North America and New Zealand and the peace philosophy of the U.S. Mennonite community, restorative justice seeks to involve all impacted by harmful doing to repair harm and “put things as right as possible” (Zehr, 2002, p. 37). Over the past several decades the use of restorative justice to build a positive school climate and address youth misconduct has grown exponentially. While schools implement restorative justice in different ways, many experts view the whole school approach to implementation as the most successful (Armour, 2015b; Gonz� alez, 2015; McCluskey et al., 2011; Riestenberg, 2012; Thorsborne and Blood, 2013). The whole school approach to restorative justice is a paradigm shift from managing student misconduct to a proactive systematic response to underlying community issues, within and beyond the school community, causing the behavior (Thorsborne and Blood, 2013). The emphasis is on building a relational ecology, which relies on relation­ ships and belonging, rather than fear of punitive consequences, to motivate change (Kidde and Alfred, 2011; Morrison and Vaandering, 2012). Ultimately, this investment in relational ecology enables a school community affected by harm or wrongdoing to address it through

Fig. 1. Application of Krieger’s ecosocial theory to understanding factors impacting high school graduation and ultimately health outcomes (Todic, Cubbin, Armour, 2019); Reprinted by permission from Taylor and Francis Group LLC (Books) US. 2

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“nurturing the human capacity for restitution, resolution, and recon­ ciliation” (Morrison and Vaandering, 2012, p. 140). As a multicomponent intervention aiming to build a closely-knit, radically inclusive school community, whole school restorative justice requires supportive school policies and full engagement from adminis­ trators, educators, support staff, students and families. The approach usually includes three sets of practices: developing social and emotional capacity in classrooms and throughout the entire school; managing difficulties and disruptions through restorative conversations, mini conferences, and problem-solving circles; and repairing serious harm through restorative conferencing, restorative mediation, class confer­ ences, and healing circles (Thorsborne and Blood, 2013). “Circles” are an essential component of all restorative practices. They offer a facili­ tated group process to youth and adults who are interested in resolving conflict, receiving or offering support, problem-solving, or making de­ cisions (Roca, Inc., 2004). Circles are spaces for dialogue, listening and storytelling. Ultimately, circles promote healing through individual and collective responsibility and “justice through ‘getting well’ as opposed to ‘getting even’” (Pranis et al., 2003, p. 1). While most school-based restorative processes rely on adult circle keepers—facilitators trained in restorative values and processes—some also engage youth as peer �lez et al., 2018; Jain et al., 2014). Gonza �lez, Sattler circle keepers (Gonza and Buth (2018) argue that peer restorative justice model actually “democratizes restorative justice” as it departs form the adult-designed and adult-led dominant model (p.207). With its emphasis on interdependence, intentional examination of power, and meaningful response to harm, restorative justice also offers a blueprint for transforming schools as structural determinants of health. Studies suggest that restorative practices may: 1) reduce school sus­ pensions and expulsions; 2) improve academic achievement and atten­ dance; 3) reduce school violence and percieved misbehavior; and 4) contribute to a positive institutional environment through supporting positive peer and youth-adult relationships, facilitating youth’s and adult’s social-emotional development, engaging parents, improving teachers’ morale, creating a calm environment, and reducing inequities associated with harsh school discipline (Fig. 2; Armour, 2014, 2015a; �lez et al., 2018; Armour and Todic, 2016; Augustine et al., 2018; Gonza Gregory et al., 2016; Jain et al., 2014; McMorris et al., 2014; Mirsky, 2007, Mirsky and Watchel, 2007; Sumner, 2010). Given this docu­ mented impact, it is plausible that restorative practices may offset the �lez et al., 2019) and also effects of adverse childhood experiences (Gonza reduce allostatic load. Studies demonstrate that, among other factors, social integration and social support can reduce the impact of allostatic load, which is beneficial for health (McEwen, 2012). For example, Brody et al. (2014) found that among Black youth in rural Georgia, the asso­ ciation of increasing neighborhood poverty across adolescence with allostatic load was not significant for youth who received emotional support compared to youth who did not. Finally, the restorative justice focus on interdependence may be a structural intervention in itself because it builds collective power needed to transform the conditions

that negatively impact health (Blankenship et al., 2006). 3. The current study context California has been a leader among the states in the implementation of school-based restorative justice. Cole Middle School in Oakland piloted restorative justice in the early 2000s and, in 2010, the Oakland Unified School District passed a resolution adopting restorative justice �lez, as a system-wide alternative to zero tolerance discipline (Gonza 2012). Since then, the state’s largest districts, including Oakland, Los Angeles, San Diego and San Francisco Unified School Districts, have made significant investments in restorative justice (Washburn, 2018). These school- and district-based efforts to implement restorative justice are occurring in the context of the statewide emphasis on reducing suspensions. In 2017, the State Superintendent of Public Instruction announced that the numbers of suspended and expelled students had declined five years in a row, underscoring the role of effective instruc­ tion and new ways of thinking about discipline (California Department of Education, 2017). Finally, in addition to this robust state-wide restorative justice implementation context, the California School Climate, Health, and Learning Survey (CAL-SCHLS)–a statewide survey data that includes three interrelated surveys targeting students, parents, and school staff–enables testing of the association between restorative justice in K-12 schools and health. To date, no published studies have investigated the association between restorative justice in schools and student health outcomes. This study aims to answer the following research questions: (1) What is the effect of school-based restorative justice within one school district with a funded restorative justice policy on middle and high school students’ (i) physical and mental health out­ comes; and (ii) academic achievement? (2) Does school-based restorative justice moderate the relationship between the individual-level predictors and the middle and high school students’ (i) physical and mental health outcomes and (ii) academic achievement? 4. Methods 4.1. Sample and data collection We used the 2013/2014 California Healthy Kids Survey (CHKS), a part of the California School Climate, Health, and Learning Survey (CALSCHLS) System. The system, developed for the California Department of Education, includes three interrelated surveys targeting students, par­ ents, and school staff. WestEd (2018) administers the surveys and makes the data available to researchers who pay a small fee and sign a Mem­ orandum of Understanding to preserve confidentiality. For this study, we used only a subsample of 6992 students nested in 32 middle and high schools in one California school district, which has a district-wide

Fig. 2. Restorative justice and pathways of embodiment. 3

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Table 1 Independent variables, dependent variables, and datasets. Independent Variable Level-2: Schools

Whole School Restorative Justice Peer Restorative Justice

Level-1: Students

School SES School Size % of Enrolled Black Students % of Enrolled Students Receiving Free or Reduced Price Meal (FRPM) Grade Ethnicity Sex Parent Education Level

Dependent Variables Mental Health Physical Health Academic Achievement Datasets

Use of whole school approach. Whole school approach includes a continuum of practices that aim to address conflict and transform the entire school climate through a continuum of restorative practices that engage students and adults. Coded 0/1. Use of peer restorative justice. Peer restorative justice refers to a site-based youth facilitator team that participates in community building and conflict resolution. Coded 0/1. Mean School SES; percentage of students receiving Free or Reduced Lunch (FRL). <300, 300–600, >600, >999 enrolled students, Coded 0–3. The proportion of students within each school who are Non-Hispanic Black. Proportion of students within each school who are receiving free or reduced meal. Student’s grade level at the time of data collection. Combined: “Are you Hispanic or Latino origin?” Yes/No and “What is your race?” American Indian or Alaska Native, Asian, Black or African American, Native Hawaiian or Pacific Islander, White, Mixed (two or more) races, coded: 0–6. “What is your sex?” Male/Female; coded 0/1. What is the highest level of education your parents completed? (Mark the education of a parent who went the furthest in school.) No High School, High School, Some college, College, Coded 0–3. “In the past 30 days did you miss any school because you felt very sad, hopeless, anxious, stressed, or angry?” Yes/ No; coded: 1/0. “In the past 30 days did you miss any school because you felt physically sick, including problems with breathing or your teeth?” Yes/No; coded: 1/0. “During the past 12 months how would you describe the grades you mostly received in school?” Mostly As, A’s and B’s; Mostly B’s, etc. Coded: 8-1 (e.g., Mostly As ¼ 8, As and Bs ¼ 7, Mostly Bs ¼ 6).

California Healthy Kids Survey—Student Version, 2013–2014, N ¼ 552,184 California Department of Education Enrollment by School Data, 2013–2014, N ¼ 10,366 California Department of Education Unduplicated Student Poverty—Free and Reduced-Price Meal (FRPM) Data, 2013–2014, N ¼ 10,362

and can establish how much of the variation in student outcomes is determined by individual differences, school-level factors, and differ­ ences between schools and districts (Luke, 2004). Using a “30/30” rule of thumb for a multilevel model–30 groups with at least 30 individu­ als–the analysis is likely to produce accurate estimates (Hox, 2010). Out of 32 schools, only one had less than 30 students (n ¼ 12). First, we examined missing data and determined that multiple imputations (MI) were necessary as a result of a large percentage of missing data, resulting in only 67% complete cases. Most of the missing data were concentrated in parental education, the only level one socioeconomic status measure available in the dataset for this sample, which had 24% missing values. The rest of the missing values ranged from no missing values to 6.9% missing. For multilevel models in which lower level units (e.g., students) are nested within higher level units (e.g., schools), the multilevel structure must be taken into account in the imputation model. Ignoring the multilevel structure in the imputation may lead to a substantial bias in model estimates of intraclass correlations and regression coefficients (Lüdtke et al., 2017). Consequently, we utilized the Blimp application (Enders et al., 2018) for Mac and Windows, which performs multi-level imputation with mixtures of continuous, ordinal, and nominal variables. Blimp implements a fully conditional specification algorithm (also known as chained equations and sequential regression) with a latent variable formulation for incomplete categorical variables. The applica­ tion utilizes the auxiliary variables that differentiate the complete and incomplete cases to generate missing data. The multiple imputation process occurs in three phases: an imputation phase, analysis phase, and pooling phase. The imputation phase creates multiple copies of the data, each with different imputed values. According to Graham et al. (2007), the current rule of thumb is 20. The basic idea behind imputation is to use a regression model to generate plausible replacement values for each missing case and then select a value at random, using computer simu­ lation, from the possible generated variable values. Computer simula­ tions suggest that the proposed procedure works well, with trivial biases in most cases (Enders et al., 2018). After conducting 20 imputations, we used the PROC MIXED and PROC GLIMMIX procedures in SAS, Version 9.4. to conduct the analysis. We also used the MIANALYZE procedure, which combined the results of the analysis of 20 imputations and

restorative justice policy with allocated budget for implementation. Thirty-four middle and high schools in the district participated in the 2013/2014 CHKS survey. We removed two schools with nontraditional structures (an independent study and alternative discipline placement school) because they were outliers. WestEd utilizes a number of tech­ niques to ensure the data are valid. For example, the CHKS meets the anonymity criterion, which ensures validity of adolescents’ self-reported data (Williams et al., 1979). 4.2. Human subject protection We obtained a non-human subject research determination letter for the secondary data analysis from the University of Texas at Austin and the California Office of Health and Human Services Institutional Review Boards. 4.3. Variables The study uses a two-level hierarchical model to examine the effects of school-based restorative justice on physical health, mental health, and academic achievement. All predictors in the multilevel models are based on theoretical and empirical considerations. We obtained the 2013/ 2014 demographic school level variables–enrollment and the cumula­ tive percentages of African American students, Latino students, and students who receive Free and Reduced Lunch Meal (FRPM)–from the California Department of Education publicly available data. The districtbased restorative justice measures–se of the whole school approach and peer restorative justice–are publicly available through the district’s web page. The correlation between whole school restorative justice and peer restorative justice is r ¼ 0.02, p < .01. Table 1 includes all independent and dependent variables. 4.4. Analysis Both the structural properties of the data (e.g. students nested within schools) and the ecosocial multilevel theoretical framework suggest the use of HLM. Multilevel analysis takes into the consideration the context 4

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generated valid statistical inferences (SAS Institute Inc., 2017). The analysis included constructing sequential theoretically driven models. We started with models without any predictor variables (null models) to first determine the between-school variance in outcomes and estimate an intraclass correlation coefficient (ICC). ICC refers to the proportion of variance in the outcomes due to differences between schools rather than differences across students within schools (Hox, 2010). We first modeled all Level-1 variables, followed by contextual Level-2 variables, and restorative justice Level-2 variables. We then combined all level 1 and level 2 variables into one model. We investi­ gated random intercept and random slope effects. We also tested cross level interactions between individual level predictors and Level-2 restorative justice predictors. We kept the interactions that were sig­ nificant at p < .01. Furthermore, we used the Laplace estimation for the logistic models and maximum likelihood estimation (ML) for the linear models. Both allow for a comparison of nested model fit statistics to determine the best model for the study data. We evaluated each model’s fit with each model’s fit with the 2 log likelihood (-2LL) and Akaike’s Information Criterion (AIC). Finally, in multilevel regressions, the intercept is the expected value of the outcome variable when the pre­ dictor values are all 0. To make interpretation more meaningful, we grand mean-centered all continuous variables for more straightforward interpretation. We dummy coded sex and ethnicity. We coded male and Black as 0, based on prior literature demonstrating that they would be at highest risk.

Table 2 Descriptive statistics for students and schools based on one set of imputed data. Student Level N ¼ 6992 M(SD) or % Independent Variables Sex (female) Ethnicity Non-Hispanic American Indian & Alaska Native (AIAN) Non-Hispanic Asian Non-Hispanic Black (ref) Non-Hispanic Native Hawaiian & Pacific Islander (NHPI) Hispanic/Latino Non-Hispanic Multiracial Non-Hispanic White Parent education
5. Results Table 2 includes descriptive statistics. After conducting multiple imputations, the sample included twenty datasets containing 6992 complete student cases nested in 32 schools. Nearly two thirds of schools were medium size, with 301–599 enrolled students. Among the 32 schools, 78% have had some level of restorative justice implementation, with years of implementation ranging from 0 to 4. A quarter of schools used peer restorative justice and almost two thirds used the whole school approach to restorative justice implementation. Among the 6992 stu­ dents, 42% have missed school in the past 30 days as a result of adverse physical health and 10% as a result of adverse mental health. The selfreported average grade for the students in the past 12 months was 4.57 (5 ¼ “Bs and Cs,” 4 ¼ “Mostly Cs”). Intraclass correlation (ICC), calculated for each model, revealed small, but statistically significant differences between schools for all three outcomes. In the following section, I present results for each of the three models.

Peer RJ (yes) Whole School RJ (yes) Dependent Variables Missed school in past 30 days due to mental health (yes) Missed school in past 30 days due to physical health (yes) Academic achievement (average 12-month grade, Coded 1–8, 8 ¼ As)

School Level N ¼ 32 M(SD) or %

51% 1% 17% 23% 2% 40% 10% 7% 31% 23% 15% 31% 35% 65% 22% 60% 9% 9% 0.73 (0.10) 25% 60% 10% 42% 4.57 (1.90)

restorative justice was protective in level two models containing only restorative justice variables (Model 4) and restorative justice variables combined with contextual school variables (Model 5), it was no longer statistically significantly protective once we included the level one in­ dividual variables into the analysis (Model 6). The mental health out­ comes model has no random slopes or statistically significant cross-level interactions.

5.1. Physical health

5.3. Academic achievement

Schools account for approximately 2% of the variability in students’ physical health-related absences and the differences are statistically significant (τ00 ¼ 0.05, p < .01). From the combined model (Table 3), girls have about 40% higher odds of physical health-related absences compared with boys. Asian students have 44% lower odds compared with Black students. Each grade increase is associated with 8% higher odds. The whole school approach to restorative justice implementation is protective, reducing the odds of physical health-related absences by 16%. There are no random slopes or statistically significant cross-level interactions in the physical health outcomes model.

Schools account for 8% of the variability in students’ overall average grade, which is also statistically significant (τ00 ¼ 0.30, p < .01). The model estimates are in Table 5. From the combined model, the average African American boy’s overall grade in a school with no RJ in this district is 3.89, with four being equal to receiving Bs and Cs and 3 being equal to receiving mostly Cs. Girls have a higher average grade than boys by 0.44. Compared to African American students, Latino students’ average grade is 0.25 points higher, multiracial students’ 0.34 points higher, NHPI students’ 0.38 points higher, Asian students’ 1.32 points higher, and White students’ 1.40 points higher. An increase in parental level of education by one credential (e.g., finishing high school to fin­ ishing some college) is associated with a 0.18 average grade increase. Furthermore, the use of peer restorative justice in a school predicts an increase in the average grade by 0.52 points, compared to school not using restorative justice. The academic achievement model has no random slopes or cross-level interactions.

5.2. Mental health Schools account for approximately 8% of the variability in students’ mental health-related absences, which is also statistically significant (τ00 ¼ 0.29, p < .01). From the combined model (Table 4), girls have 2.5 times higher odds of mental health-related absences compared with boys. Multiracial students have approximately 60% higher odds and Latino students have 30% higher odds compared with Black students. Each grade increase is associated with 21% higher odds. While peer 5

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Table 3 Estimates from two-level generalized linear dichotomous model predicting physical health outcomes (N ¼ 6992).

Fixed Effects Intercept Level 1 Sex (male ref) Ethnicity (Black ref) AIAN Asian Non-Hispanic White NHPI Hispanic/Latino Multiracial Parent education Grade % of Black Students % of FRPM eligible students School size Peer RJ Whole School RJ Error Variance Level-2 Intercept

Model 1 Unconditional OR (95% CI)

Model 2 Level 1 Variables OR (95% CI)

Model 3 Level 2 Contextual Variables OR (95% CI)

Model 4 Level 2 RJ Variables OR (95% CI)

Model 5 All Level 2 Variables OR (95% CI)

Model 6 Combined Model OR (95% CI)

0.70** (0.63–0.78)

0.65** (0.57–0.74) 1.41** (1.28–1.57)

0.72**(0.64–0.80)

0.77**(0.67–0.89)

0.79** (0.69–0.89)

0.68** (0.58–0.79) 1.42** (1.29–1.56)

0.89 (0.49–1.60) 0.55** (0.47–0.66) 1.11 (0.89–1.36)

0.88 (0.49–1.60) 0.56** (0.47–0.66) 1.10 (0.88–1.37)

0.87 (0.58–1.28) 0.99 (0.87–1.14) 1.14 (0.94–1.36) 1.04 (0.99–1.09) 1.08** (1.05–1.13)

1.78** (1.15–2.77) 0.57 (0.33–0.99)

0.88 (0.59–1.31) 1.02 (0.89–1.17) 1.13 (0.94–1.36) 1.04 (0.98–1.10) 1.08** (1.04–1.13) 1.33 (0.86–2.08) 0.72 (0.42–1.25)

0.97 (0.89–1.06) 0.97 (0.81–1.15) 0.82** (0.71–0.96) 1.02*

0.99 (0.91–1.08) 1.13 (0.93–1.36) 0.84* (0.72–0.97) 1.01

1.85* (1.15–2.96) 0.58 (0.32–1.03) 0.98 (0.89–1.09)

1.05**

1.03*

1.03 (0.83–1.26) 0.81*(0.67–0.97) 1.04*

1.03*

Note: **p < .01. *p < .05, � <0.1; Model 2 includes all L-1 variables; Model 3 includes all L-2 variables; Model 4 includes all L1 and L2 variables; ICC ¼ 1.5%; Values are based on SAS PROC GLIMMIX; Entries show odds ratios with 95% CI in parenthesis; Estimation model ¼ Laplace. Table 4 Estimates from two-level generalized linear dichotomous model predicting mental health outcomes (N ¼ 6992).

Fixed Effects Intercept Level 1 Sex (male ref) Ethnicity (Black ref) AIAN Asian Non-Hispanic White NHPI Hispanic/Latino Multiracial Parent education Grade Level 2 % of Black Students % of FRPM-eligible Students School size Peer RJ Whole School RJ Error Variance Level-2 Intercept

Model 1 Unconditional

Model 2 Level 1 Variables

Model 3 Level 2 Contextual Variables

Model 4 Level 2 RJ Variables

Model 5 All Level 2 Variables

Model 6 Combined Model

0.09** (0.07–0.11)

0.05** (0.04–0.07)

0.09** (0.07–0.12)

0.11* (0.08–0.16)

0.12** (0.09–0.16)

0.06** (0.04–0.80)

2.45** (2.08–2.92)

2.46** (2.08–2.91)

1.39 (0.53–3.67) 0.75* (0.56–0.99) 1.16 (0.82–1.64) 0.96 (0.50–1.85) 1.25� (1.00–1.57) 1.58 (1.20–2.10) 1.02 (0.93–1.11) 1.22** (1.15–1.30)

1.40 (0.53–3.69) 0.77 (0.58–1.02) 1.18 (0.84–1.67) 0.98 (0.51–1.88) 1.29* (1.02–1.63) 1.59** (1.20–2.10) 1.01 (0.93–1.11) 1.20** (1.12–1.28) 2.65 (0.87–8.09) 0.90 (0.22–3.60) 1.00 (0.77–1..27)

1.34**

1.09

1.28**

0.61* (0.39–0.95) 0.80 (0.54–1.18) 1.22*

3.03* (1.24–7.44) 0.80 (0.26–2.51) 1.01 (0.83–1.22) 0.56** (0.38–0.83) 0.81 (0.57–1..13) 1.14*

2.00 (0.93–4.30) 0.95 (0.36–2.52) 1.01 (0.88–1.16) 0.83 (0.58–1.19) 0.89 (0.68–1.15) 1.05

Note: **p < .01. *p < .05, � ¼ 0.05; Model 2 includes all L-1 variables; Model 3 includes all L-2 variables; Model 4 includes all L1 and L2 variables; ICC ¼ 8.2%; Values are based on SAS PROC GLIMMIX; Entries show odds ratios with 95% CI in parenthesis; Estimation model ¼ Laplace.

6. Discussion

analysis using the 2013/2014 California Healthy Kids Survey data for one school district in California confirmed the study hypothesis partially. Compared to the schools not using restorative justice, the whole school approach to restorative justice implementation was associated with 16% reduced odds of missing school due to physical health prob­ lems. In addition, compared to students who attended schools that did not use peer restorative justice, those attending schools that used it had a higher 12-month average grade by 0.52 points. To contextualize the size of this effect, one point represents a shift from a student receiving Bs and Cs to receiving mostly Bs. Restorative justice was not associated with reduced odds of missing school due to mental health reasons when in­ dividual and school-level variables were in the model; however, peer

The aim of this study was to investigate the effect of school-based restorative justice on middle and high school students’: (i) physical and mental health outcomes and (ii) academic achievement as a healthassociated factor. We hypothesized a positive association between implementation of restorative justice in K-12 schools and physical health, mental health, and academic success. We also hypothesized that restorative justice moderates the relationship between individual pre­ dictors and these outcomes. More specifically, we expected that restor­ ative justice will benefit boys and African American, American Indian, and Latino student more because they bare the disproportionate impact of punitive school discipline and adverse school climate. Multilevel 6

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Health and Place xxx (xxxx) xxx

Table 5 Estimates from two-level linear model predicting academic achievement (N ¼ 6992).

Fixed Effects Intercept Level 1 Sex (Male ref) Ethnicity (Black ref) AIAN Asian Non-Hispanic White NHPI Latino Multiracial Parent education Grade Level 2 % of Black Students % of FRPM eligible students School size Peer RJ Whole School RJ Error Variance Level 1 Level 2

Model 1 Unconditional

Model 2 Level 1 Variables

Model 3 Level 2 Contextual Variables

Model 4 Level 2 RJ Variables

Model 5 All Level 2 Variables

Model 6 Combined Model

4.56** (0.10)

4.00** (0.10) 0.44** (0.04)

4.71**(0.13)

4.52** (0.13)

4.58** (0.14)

3.89** (0.18) 0.44** (0.04)

0.14 (0.27) 1.33** (0.07) 1.40** (0.10) 0.39* (0.18) 0.26** (0.06) 0.34** (0.08) 0.18** (0.02) 0.03 (0.02)

0.85 (0.50)

1.05** (0.40)

0.94 (0.52) 0.11 (0.11)

3.45** (0.06) 0.30** (0.09)

3.12** (0.05) 0.24** (0.07)

3.45** (0.06) 0.22** (0.06)

0.15 (0.27) 1.32** (0.07) 1.40** (0.10) 0.38* (0.18) 0.25** (0.06) 0.34** (0.08) 0.18** (0.02) 0.04* (0.02) 1.03* (0.41)

0.48*(0.02) 0.20 (0.18)

0.81 (0.47) 0.07 (0.10) 0.51** (0.18) 0.13 (0.16)

0.16 (0.47) 0.02 (0.10) 0.52** (0.19) 0.09 (0.16)

3.45** (0.06) 0.24** (0.07)

3.45** (0.06) 0.16**(0.05)

3.12**(0.05) 0.16** (0.05)

Note: Note: **p < .01. *p < .05, � <0.1; Model one includes all L-1 variables; Model 2 includes all L-2 variables; Model 3 includes all L1 and L2 variables; ICC ¼ 8%; Values are based on SAS PROC MIXED; Entries show parameter estimates with standard errors in parenthesis; Estimation model ¼ ML.

restorative justice appeared to be protective in level 2 models and its association with mental health outcomes should be investigated further. Improved school attendance matters because it ensures the student’s opportunity to learn, the strongest predictor of academic achievement (Skiba et al., 2014). Students living in poverty (Gee, 2018), students witnessing or experiencing neighborhood violence (Stempel et al., 2017), students who identify as Multiracial, Pacific Islander, Native American, African American or Hispanic (Gee, 2018) and students with disabilities (Black and Zablotsky, 2018) experience a number of distinct challenges tied to structural barriers and have higher odds of chronic absenteeism than their counterparts. Chronic absenteeism (missing 15 or more days of school) is associated with a number of adverse academic outcomes, including course failure and high school dropout (Jacob and Lovett, 2017). Graduating with a regular high school degree matters for health: students who do, have better health outcomes than those who drop out or get a GED (Zajacova and Everett, 2014; Zajacova et al., 2012). While obtaining at least a bachelor’s degree is generally associ­ ated with the best health outcomes, finishing high school is a critical interim step. The overall US adjusted cohort graduation rate (ACGR) for public high schools–the percentage of high school students graduating with a regular high school diploma within four years of starting 9th grade–has improved over time; however, ACGR varies by ethnicity, with notably lower rates for Latino (78%), Black (75%), and American Indi­ an/Alaska Native (72%) students, compared to Asian/Pacific Islander (90%) and White (88%) students (National Center for Education Sta­ tistics, 2017). These disparities in high school graduation generally mirror health inequities (Centers for Disease Control and Prevention, 2015). This study findings related to the positive effects of peer restorative justice are especially noteworthy. A sizable body of literature—scholarly and gray—states that the whole school approach to restorative justice implementation is the best practice for integrating it into school systems. �lez et al., However, just a handful of websites and a few articles (Gonza 2018; Jain et al., 2014; Sandel et al., 2013) discuss the benefits of peer restorative justice for building healthier school climate. Developing youth leaders as restorative practitioners offers a pathway to meaning­ fully engage them in transforming school climate, while challenging youth marginalization and adultism. Adultism refers to behaviors and attitudes that are based on the assumptions that “adults are better than young people, and entitled to act upon young people without their

agreement. This mistreatment is reinforced by social institutions, laws, customs, and attitudes” (Bell, 2013, p.542). Zero-tolerance-policies offer just one of many examples of the negative impact that adultism has on young people’s lives. As a result, many youth feel powerless, dis­ respected, and dismissed (Bettencourt, 2018), often internalizing and reproducing the adultist perspective (Conner et al., 2016). In that sense, youth’s experiences with adultism serve as a foundation for socializing them into larger structures of social inequality (Bettencourt, 2018; DeJong and Love, 2015). Because peer-led restorative justice processes emphasize youth voice and youth/adult equity, they have the potential to transform typical adultist power dynamics, which in turn offers a pathway for transforming other power dynamics that sustain structural inequality. Our findings did not support our hypothesis that restorative justice moderates the relationship between individual predictors—sex and ethnicity–and benefit boys, African American, American Indian, and Latino students more because they are disproportionately impact by punitive school discipline. Research suggests that higher rates of sus­ pensions and expulsions decrease feelings of safety and lead to less satisfactory ratings of school climate for all students (Skiba et al., 2014; American Psychological Association Zero Tolerance Task Force, 2008). Furthermore, Perry and Morris (2014) found that schools with high rates of exclusionary discipline over time generate “collateral damage,” also negatively affecting the reading and math achievement of students who are not suspended. In that sense, our findings support the notion that school-based restorative justice may be an effective approach to improving educational and health outcomes for all youth. However, given that most schools use restorative justice to reduce the dispropor­ tionate impact of harsh discipline practices on students of color and students with disabilities, the absence of moderating effect was sur­ prising. Three factors should be considered in interpreting this fin­ ding—the length of restorative justice implementation in schools included in this study, theory guiding implementation, and ecosocial context. The current literature suggests that fully implementing restor­ ative justice in a school can take up to five years (Morrison, Blood & Thorsborne, 2006), but the average length of implementation in this study was less than a year. As a result, there may be lagged effects to implementation, which are not yet reflected in the findings. Our findings also provide further support for the recommendation that school-based restorative justice implementation efforts must center the role of power 7

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and institutionalized oppression in school discipline outcomes. A num­ ber of scholars have provided instructive suggestions for how to engage in that process. For example, Lustick (2017) offers culturally responsive pedagogy and a Foucauldian lens as helpful tools in constructing spaces in which students and staff can honestly reflect on their actual contri­ butions to the conflict and repair authentic relationships, rather than “the traditional power structures of schools” (p. 308). Others, like Vandeering (2010), Anyon et al. (2018), and Wadhwa (2010, 2017) recommend the use of critical theory and critical race theory in restor­ ative work in schools. They suggest that the critical theoretical approach—which requires an explicit focus on understanding the rela­ tionship between institutional forms of oppression such as racism and student outcomes—must inform planning, implementation, and evalu­ ation of school-based restorative justice to ensure that the efforts actu­ ally reduces inequities. Finally, Crenshaw and colleagues’ (2015) make a strong case for the use of intersectional feminist critical analysis to ensure human rights of all youth disproportionately impacted by puni­ tive discipline practices, especially girls of color. Consequently, restor­ ative practices, enhanced by these lenses, could indeed fulfill its potential to address inequity and truly transform schools as structures that impact health. Finally, school-based restorative justice, although fast-growing, is relatively young (Gonz� alez, 2012). Expecting it to counter centuries of structural racism reflected in schooling, including discipline, may be problematic. It may be that changes in discipline disproportionality will require use of restorative justice rooted in a critical theory over an extended period of time.

transforming schools as structures that impact health. While the whole school approach to restorative justice implementation was a predictor of several examined outcomes, peer restorative justice, which is less emphasized in the literature, also emerged as a significant predictor of positive health-associated outcomes. Peer restorative justice therefore should be investigated further through qualitative and quantitative approaches. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments We thank Catherine Duarte for reviewing the advanced version of the manuscript and providing helpful comments which have improved its quality. References Advancement Project, 2014. Restorative practices: fostering healthy relationships & promoting positive discipline in schools. Retrieved from. https://advancementpro ject.org/resources/restorative-practices-fostering-healthy-relationships-promotin g-positive-discipline-in-schools/. Advancement Project, 2010. Test, punish, and push out: how “zero tolerance” and highstakes testing funnel youth into the school-to-prison pipeline (revised). Retrieved from. https://www.ushrnetwork.org/sites/ushrnetwork.org/files/test_punsih_ push_out.pdf. American Academy of Pediatrics, Council on School Health, 2013. Out-of-school suspentions and expulsions. Retrieved from. http://pediatrics.aappublications.org/ content/early/2013/02/20/peds.2012-3932. American Psychological Association Zero Tolerance Task Force, 2008. Are zero tolerance policies effective in the schools?: an evidentiary review and recommendations. Am. Psychol. 63 (9), 852. American Public Health Association, 2018. Chronic stress and the risk of high school dropout. Retrieved from. http://www.schoolbasedhealthcare.org/-/media/files/ pdf/sbhc/chronic_stress.ashx?la¼en&hash¼F5FB7AF535D2CDA4CBC81236D BCE6580B53607E4. Anyon, Y., Jenson, J.M., Altschul, I., Farrar, J., McQueen, J., Greer, E., Simmons, J., 2014. The persistent effect of race and the promise of alternatives to suspension in school discipline outcomes. Child. Youth Serv. Rev. 44, 379–386. Anyon, Y., Lechuga, C., Ortega, D., Downing, B., Greer, E., Simmons, J., 2018. An exploration of the relationships between student racial background and the school sub-contexts of office discipline referrals: a critical race theory analysis. Race Ethn. Educ. 21 (3), 390–406. Armour, M., 2015b. Restorative practices: Righting the wrongs of exclusionary school discipline. U. Rich. L. Rev. 50, 999. Armour, M., Todic, J., 2016. Restorative Practices at a Charter K-3 Elementary School First Year Implementation Evaluation. Institute for Restorative Justice and Restorative Dialogue, The University of Texas at Austin, Austin, TX. Armour, M., 2015a. Ed White Middle School Restorative Discipline Evaluation: 2014/ 2015. Institute for Restorative Justice and Restorative Dialogue, The University of Texas at Austin, Austin, TX. Armour, M., 2014b. Ed White Middle School Restorative Discipline Evaluation: 2013/ 2014. Institute for Restorative Justice and Restorative Dialogue, The University of Texas at Austin, Austin, TX. Armour, M., 2014a. Restorative disciple: a whole school approach. In: Paper Presented at the 10th Annual Texas Behavior Support State Conference, Houston, TX. Augustine, C.H., Engberg, J., Grimm, G.E., Lee, E., Wang, E.L., Christianson, K., Joseph, A.A., 2018. Can Restorative Practices Improve School Climate and Curb Suspensions?. An Evaluation of the Impact of Restorative Practices in a Mid-sized Urban School District. Rand, Santa Monica, CA. Balfanz, R., Byrnes, V., Fox, J.H., 2015. Sent home and put off track. In: Losen, D. (Ed.), Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion. Teachers College Press, New York, pp. 17–30. Bell, J., 2013. Understanding adultism: a key to developing positive youth-adult relationships. In: Adams, M., Blumenfield, W., Castaneda, C., Haclman, H., Peters, M., Zuniga, X. (Eds.), Readings for Diversity and Social Justice, third ed. Routledge, New York, pp. 542–549. Bettencourt, G.M., 2018. Embracing problems, processes, and contact zones: using youth participatory action research to challenge adultism. Action Res. https://doi.org/ 10.1177/1476750318789475. Black, L.I., Zablotsky, B., 2018. Chronic School Absenteeism Among Children with Selected Developmental Disabilities: National Health Interview Survey, 2014-2016. National Health Statistics Reports. Number 118. National Center for Health Statistics. Blankenship, K.M., Friedman, S.R., Dworkin, S., Mantell, J.E., 2006. Structural interventions: concepts, challenges and opportunities for research. J. Urban Health 83 (1), 59–72.

6.1. Limitations No cause and effect can be evaluated given the cross-sectional nature of the data. This study relies on a single unique school district population and therefore has limited generalizability. Future studies should focus on modeling more representative data. Furthermore, this study, as all secondary analysis of existing data, relies on available data not collected to address the particular research question or to test this particular hy­ pothesis. Many critical variables –teacher- and school-related– were not available for the analysis. Furthermore, this study uses health outcomes proxies such as missing schools due to health problems as opposed to actual health outcomes. In the future, it will be important to examine the association between restorative justice and actual health outcomes such as self-reported health, depression, and behavioral health outcomes. We also did not collect the data used in the analysis and may be unaware of study-specific nuances or glitches in the data collection process that may be important to the interpretation of specific variables in the dataset (Cheng and Phillips, 2014). We attempted to mitigate these issues by carefully reviewing West Ed’s information about the validity of the data. Finally, the short period of implementation (less than a year on average) and the possible associated lagged effects to implementation discussed above must be considered in interpretation of our findings. 6.2. Conclusion While restorative justice implementation was a significant predictor for several of the outcomes examined in the study, it is critical to point out that only up to 8% of differences in mental health outcomes, physical health outcomes, and academic achievement were between schools, leaving the vast majority of the difference to be explained by individual and/or other contextual factors. Consequently, it is imperative that those invested in achieving health equity and using restorative justice in schools as a structural intervention, simultaneously focus on other points of intervening beyond schools. Fig. 1 provides an instructive framework for considering other points of intervention, such as eco­ nomic opportunities and community conditions, aimed at improving educational and health outcomes for all students and their families. At the same time, this study provides a critical pathway for the role that restorative justice, and especially peer restorative justice, can play in 8

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