- 1Department of Psychology, University of Nevada, Las Vegas, Las Vegas, NV, United States
- 2Department of Developmental Psychology and Teaching, University of Alicante, San Vicente del Raspeig, Alicante, Spain
School attendance has been historically linked to healthy states of functioning, whereas school attendance problems/absenteeism have been historically linked to unhealthy states of functioning. Indeed, school attendance and its problems are deeply embedded within multiple domains of functioning at both analytic and systemic levels. This article utilizes complex systems theory and the concept of early warning signals to illustrate how changes in school attendance could indicate instability and perhaps sudden transitions to unhealthy states of functioning for students, families, schools, and communities. The article reviews how school attendance problems/absenteeism intersect with functioning at analytic (academic, social–emotional, mental health, physical health, family) and systemic (school and community) levels. The article also includes recommendations for how viewing changes in school attendance as early warning signals could improve health-based protocols (enhancing access to care; integrating systems of care) and school-based practices (developing multi-tiered systems of support models and community asset maps; modifying educational and policy perspectives). A primary theme involves more streamlined efforts to identify movement from healthy to unhealthy states among individuals to assign proactive and personalized treatment avenues (health-based protocols) and among systems to enact needed intervention supports and reforms (school-based practices).
1. Introduction
Childhood education is a core aspect of the United Nations Declaration of Human Rights (Article 26, United Nations General Assembly, 1948). Formal or informal school attendance is thus a fundamental aspect of daily life for most youths worldwide. School attendance in the modern era involves many forms across different instructional formats, including in-person presence at home or in a designated building for educational purposes; full participation, positive engagement, and adequate competency-based attainment in virtual/distance/remote learning programs; or a mixture of physical presence and participation/engagement/attainment in blended or hybrid learning programs (Patrick and Chambers, 2020; Li and Wang, 2022). School attendance can occur as well within the context of residential, inpatient, juvenile justice, and alternative educational and other facilities (Kumm et al., 2020; Knollmann et al., 2022).
Conversely, school attendance problems (SAPs) also impact many youths worldwide. SAPs in the modern era include, for in-person learning formats, complete absence from school on one or more days (absenteeism), partial absences from school (e.g., via missed classes or sections of a school day), tardiness to school, morning misbehaviors designed to miss school, and/or substantial mental/physical health challenges as well as structural and operational barriers that preclude formal school attendance (Kearney et al., 2023). SAPs can also include, for virtual/distance/remote learning formats, and particularly post-pandemic, missed log-ins, limited number of interfacing hours per day, incomplete assignments, delayed timelines for meeting course objectives, problematic student-teacher interactions, and deficits in measures of competency, mastery, and achievement, among other metrics (National Forum on Education Statistics, 2021).
School attendance and SAPs/absenteeism have been studied by professionals across myriad disciplines over the past century, which has produced a wide swath of perspectives on these issues. These perspectives can be grouped generally into analytic approaches that focus on specific contexts and narrow-band variables that impact school attendance/SAPs/absenteeism as well as systemic approaches that focus on overarching contexts and broad-band variables that impact school attendance/SAPs/absenteeism (Kearney, 2021). Analytic approaches in this area often focus on microsystem (immediate environment) and mesosystem (interconnected microsystems) ecological impacts such as student health, caregiver-student interactions, family-school communications, and peer victimization (Pengpid and Peltzer, 2019; Lee et al., 2023). Systemic approaches in this area often focus on exosystem (social structures) and macrosystem (cultural elements) ecological impacts such as school climate, educational policy, transportation schemes, and economic patterns (Berkowitz et al., 2017; Stein and Grigg, 2019). Both approaches also focus on chronological ecological impacts as students advance through different levels of education (e.g., preschool to elementary school, grades, primary to secondary) and encounter new developmental challenges at each level (Smerillo et al., 2018). Health-based protocols that address attendance/SAPs/absenteeism often draw from analytic approaches, whereas school-based practices that address attendance/SAPs/absenteeism often draw from systemic approaches (Wilkins and Bost, 2016; Melvin et al., 2019).
Analytic and systemic approaches to school attendance/SAPs/absenteeism have produced a rich literature base as well as fundamental results pertinent to healthy and unhealthy functioning (Kearney et al., 2022). At an analytic level, school attendance has been generally linked to student benefits and SAPs/absenteeism have been generally linked to student impairments across critical domains of functioning (e.g., academic, social–emotional, mental health, physical health, family) (Ansari and Pianta, 2019). At a systemic level, school attendance rates are often linked to enhanced school funding and are an important federal marker for accountability purposes. Conversely, school absenteeism rates are a federal marker of reduced school quality (Darling-Hammond et al., 2016). In addition, schools and surrounding communities with high chronic absenteeism rates are commonly associated with resource-deprived, insalubrious, and unsafe climates and environments (Sugrue et al., 2016).
At an analytic level, eventual school completion/graduation is associated with long-term individual benefits into adulthood, especially opportunities for more advanced levels of education and greater earning potential (Lara et al., 2018). Conversely, school dropout (permanent departure from school prior to completion) is associated with long-term impairments into adulthood, including greater difficulties across economic, health, interpersonal, legal, occupational, and psychiatric domains (Lansford et al., 2016; Rocque et al., 2017; Ansari et al., 2020; Rumberger, 2020). At a systemic level, school retention and graduation rates are closely associated with positive school and community variables such as constructive student-teacher relationships, school-sponsored activities, greater connectedness, high-quality and varied educational options, and supportive social networks (Zaff et al., 2017). Conversely, school and communities with high school dropout rates are commonly associated with substantial barriers to school completion; examples include deliberate student exclusion, pervasive and biased exclusionary discipline practices, transportation vulnerabilities, and lack of access to rigorous coursework, instructional materials, and supportive technologies (DePaoli et al., 2018; UNESCO, 2019).
An intricate and deep entwinement exists of school attendance with healthy states of functioning and SAPs/absenteeism with unhealthy states of functioning at both analytic and systemic levels. Such entwinement raises the possibility that SAPs/absenteeism could be utilized as salient early warning signals for diagnostic/conceptual, assessment/evaluation, and treatment/intervention purposes across health-and school-based settings. A key potential advantage of this approach would be more streamlined efforts to identify movement from healthy to unhealthy states among individuals to assign proactive and personalized treatment avenues (health-based protocols) and among systems to enact needed intervention supports and reforms (school-based practices). The next section introduces complex systems theory and the concept of early warning signals to help guide these efforts. Subsequent sections include a review of how SAPs/absenteeism intersect with functioning at analytic (academic, social–emotional, mental health, physical health, family) and systemic (school and community) levels. The article concludes with recommendations for how SAPs/absenteeism as early warning signals may improve certain health-based protocols (enhancing access to care; integrating systems of care) and school-based practices (developing multi-tiered systems of support models and community asset maps; modifying educational and policy perspectives).
2. Complex systems theory and early warning signals
Complex systems theory has emerged to help explain how systems can undergo sudden transitions from one state to another, such as from healthy to unhealthy status. Examples include abrupt onsets of epidemics, financial market collapses, natural disasters, and wildlife extinction events (Scheffer et al., 2009). Complex systems theory focuses on level of resilience in a system and its potential decline over time (Fraccascia et al., 2018). Such decline creates greater system instability, or increased vulnerability to perturbations or minor contextual disturbances that may propel the system past a tipping point and thus a sudden transition to an altered and possibly unhealthy state (Wichers et al., 2019). Complex systems theory has been extended to health concepts such as physical and mental functioning systems as well as to educational concepts such as the K-12 school system (Mital et al., 2014; Rutter et al., 2017). Such concepts within complex systems theory are understood as multifaceted, dynamic, and dimensional rather than as rudimentary, static, and categorical. As such, a focus is made on factors within a system that are most sensitive to instability and thus movement toward tipping points and sudden transitions (Nelson et al., 2017). With respect to health and educational systems, for example, this may involve identifying critical aspects and periods of risk for fitness deterioration or for academic failure (Jacobson et al., 2019; Wright and Woods, 2020).
Those operating from complex systems theory often focus on early warning signals, or elements associated with destabilization or critical slowing down of a particular ecological system (Clements et al., 2019). Early warning signals may serve as a tool to detect shifts in functioning ahead of time and thus possibly deter destabilization (Helmich et al., 2022). Helmich et al. (2021) issued several recommendations for exploring early warning signals and critical transitions in psychopathology, including attempts to distinguish normal variation from formal system transition, or from a healthy to an unhealthy mental state, and to identify sudden symptom shifts that preceded the system change. Sometimes the focus in this regard is on granular variables such as momentary changes in affective states (Schreuder et al., 2020). Early warning signals could also include broader constructs, however, such as prodromal states, functional deficits, and chaos indicators (e.g., Oliver et al., 2015).
Youths and their families as well as schools and communities operate in a complex ecological system with various levels of influential factors that can move them, sometimes suddenly, from a harmonious and healthy state to a discordant and unhealthy state. Disturbed functioning in an unhealthy state intersects with the clinical concept of impairment, or interference with and/or reduction in adequate system performance (Calderón-Larrañaga et al., 2019). For students and their primary caregivers, such impairment can include deterioration in academic, social–emotional, mental health, physical health, and family functioning. For schools and communities, such impairment can include deterioration in climate, engagement, resources, safety, and graduation rates, among other variables. The next sections review ways in which SAPs/absenteeism intersect with various aspects of perturbed functioning. An emphasis is placed on student and family functioning given the theme of this special series. However, a section on school and community functioning is included as well to help set the stage for later recommendations for improving school-based practices.
3. School attendance problems and absenteeism and domains of functioning
Considerable debate exists as to the direction of school attendance/absenteeism and its concomitant benefits/impairments; either can be a cause and/or a consequence of the other. This debate is set aside for now in favor of a parallel approach (i.e., concurrent school attendance/absenteeism with benefits/impairments) for practical purposes and until future empirical work specifically addresses this question for this population (Kearney, 2022). Instead, the assumption adopted for the following sections is that SAPs/absenteeism are proximal and integral features across many key domains of functioning and thus a potentially salient warning signal of an unhealthy state.
3.1. Academic functioning
Academic functioning involves a wide array of metrics and concepts such as achievement in numeracy and literacy and other subjects (including grade point average), grade progression/retention, standardized test scores, and school engagement, among others (Wang et al., 2020). School absenteeism is closely associated with deficits in many areas of academic functioning. These deficits became especially pronounced during the COVID-19 pandemic as absenteeism rates spiked (Kuhfeld et al., 2020). Many of these effects are expected to last several years, disproportionately affect vulnerable students, and intersect with variables such as teacher disengagement, lower academic expectations, and less access to academic supports (Howard-Jones et al., 2022; Kipp, 2022).
School absenteeism is associated with deficits in numeracy and literacy achievement, and these deficits appear to become cumulative over time (Ansari and Gottfried, 2021; Carroll et al., 2022). In related fashion, lower grade point average has been associated with multiple levels of absenteeism severity, and course failure with absenteeism is often a key element of early warning systems that predict later school dropout (Skedgell and Kearney, 2018; Balfanz and Byrnes, 2019). Key mechanisms in this regard could include long-term declines in executive functioning capabilities such as working memory and cognitive flexibility, social alienation, student illness and risky behaviors, and less teacher-based instruction (Fuhs et al., 2018; Gottfried and Ansari, 2021; Sosu et al., 2021; Klein et al., 2022). The relationship between school absenteeism and impaired academic achievement is highly multifaceted and complex, however, including not just many of the variables described here but also broader exosystem and macrosystem variables (e.g., biased/inappropriate academic placements; level of school funding for academic resources; youth employment to support family members economically) (Singer et al., 2021; Lee et al., 2023).
Grade retention has also been found to be associated with school absenteeism and dropout at schoolwide and individual levels, though findings vary (Hughes et al., 2018; Martorell and Mariano, 2018; Gubbels et al., 2019). Possible though not necessarily robust mechanisms of such a relationship include reduced student motivation (Rhodes et al., 2018), social disconnection and stigma (Valbuena et al., 2021), stress, and negative impact on academic self-concept and self-confidence (Goos et al., 2021). Grade retention can intersect as well, however, with school-based disciplinary policies, externalizing behavior problems, and student-teacher variables and can be disproportionately assigned by race/ethnicity (Mattison et al., 2018; Peguero et al., 2018).
Student absenteeism also negatively affects standardized test scores, particularly for core classes (e.g., English, math), later grades, and spring semester timelines, and does so across ages and demographic groups (Aucejo and Romano, 2016; Gershenson et al., 2017; Santibañez and Guarino, 2021). Key mechanisms may include absenteeism during test preparation windows as well as congestion or spillover effects when returning absentee students cause a slowing of instruction and impair educational outcomes for other students (Gottfried and Kirksey, 2017; Gottfried, 2019). Others contend that out-of-school factors such as students’ home lives account for much of the association between school absenteeism and suppressed test score achievement (Pyne et al., 2023).
School engagement is a multidimensional construct that generally refers to student effort, participation, or involvement in learning activities (Ben-Eliyahu et al., 2018). School engagement is often thought of as academic (e.g., asking questions, paying attention in class), affective (e.g., emotional state, sense of belongingness at school), behavioral (e.g., following rules, participating in school-related activities), and cognitive (e.g., investment in learning, self-regulation) in nature (Fredricks et al., 2004; Appleton et al., 2006). School disengagement, which can include school absenteeism, refers to lack of student interest and commitment to learning activities (Baiden et al., 2020). Longitudinal and other studies reveal a general pattern of school disengagement to academic failure/absenteeism to school dropout, though many reciprocal processes and nuanced pathways exist (Archambault et al., 2022; Piscitello et al., 2022). In addition, many studies reveal that early school absenteeism tends to beget later additional school absenteeism (e.g., Ansari and Pianta, 2019). Key mechanisms of these longitudinal processes could include declining self-efficacy and self-esteem with respect to academic performance, alienation from teachers, less caregiver and peer social support, and school climate and safety issues (Martinot et al., 2020).
3.2. Social–emotional functioning
Social–emotional functioning is a multifaceted construct that broadly includes self-regulation, academic mindsets, and social competence, among other personal and moral variables (Schoon, 2021). Schools are primary settings for social–emotional development and intervention for youths and so a natural, bidirectional link exists between impairment in this functional domain and school absenteeism (Sheridan et al., 2019; Lindholdt et al., 2023). In addition, frameworks to guide the conceptualization, assessment, and treatment of SAPs often include a specific focus on social–emotional functioning (e.g., Kearney and Albano, 2018; Nichols et al., 2021). Issues regarding emotional self-regulation, academic mindsets, and social competence are presented in this section. Note that some topics (e.g., aggression, association with truant peers, participation in school-based activities) in subsequent sections overlap with social–emotional functioning.
Emotional regulation refers to one’s ability to monitor, evaluate, and modify emotional responses to achieve a particular goal (Bettis et al., 2022). Youths with SAPs have been shown to demonstrate less healthy emotional regulation strategies via less use of cognitive reappraisal and greater use of expressive suppression, which may be impacted by level of parental control (Hughes et al., 2010, 2022). Emotional regulation difficulties in youths with behavior disorders have also been linked to SAPs (Classi et al., 2012; Kim and Page, 2013). Possible mechanisms or impacts for these findings include anxious and depressive symptoms and comorbid psychiatric diagnoses as well as personality characteristics such as inhibition, neuroticism, schizoid/schizotypal features, and social introversion (Van Ameringen et al., 2003; Filippello et al., 2018; Carpentieri et al., 2022; de Groot et al., 2023). In addition, these findings may intersect with others that have shown school absenteeism to relate to less self-management, self-efficacy, and social awareness, particularly among middle school students (West et al., 2018; Santibañez and Guarino, 2021). Conversely, interventions to enhance emotional self-regulation in youths have been shown to improve school attendance (Chu et al., 2015; Lall, 2020).
Academic mindset refers to attitudes and beliefs a student has with respect to their own abilities, which can affect motivation and behavior at school (Wanzer et al., 2019). Negative attitude toward school is a substantial risk factor for school absenteeism (Gubbels et al., 2019). Bacon and Kearney (2020) examined a very large sample of youths with various levels of school absenteeism via decision tree analysis. Youths with 10–20% absenteeism often expressed negative attitudes and beliefs about their ability to complete difficult or challenging work, ask questions in class, actively participate in class, hand in assignments in a timely fashion, and tolerate frustration. These students also expressed negative attitudes and beliefs about their school’s ability to notice absences, prevent bullying, and provide a safe environment and a good education. Conversely, a positive growth mindset has been found to mitigate the odds of chronic absenteeism (Malika et al., 2021). Possible mechanisms for these effects include academic tenacity, school belongingness, resilience, and transmission of mindset beliefs by teachers (e.g., Thomas et al., 2019).
Social competence refers to effectiveness in social skills as well as the presence of adaptive interpersonal relationships (Persich and Robinson, 2022). Chronic school absenteeism has been linked to greater social isolation and peer victimization, less peer support and social engagement, and fewer friendships (Gottfried, 2014; Rahman et al., 2023). Children with SAPs also exhibit greater loneliness and negative affect than their school-attending peers (Jones and Suveg, 2015). Gonzálvez et al. (2019a) found that youths with SAPs demonstrated worse social competence overall than youths with no SAPs. This effect was more pronounced among youths missing school with negative affectivity (anxiety/depression) and social aversion. Other mechanisms explaining reduced social competence with greater school absenteeism may include anxious speech patterns, difficulty concentrating, extended speech latencies, less speech, less assertiveness and friendliness, poorer social performance, greater difficulties with interpersonal relationships, and perceptions by others as less likeable and socially desirable (Scharfstein and Beidel, 2015; de Lijster et al., 2018). School-based mechanisms in this regard may include degrees of school engagement and safety, effective classroom management practices, and social–emotional learning instruction in interpersonal and related skills (Darling-Hammond and Cook-Harvey, 2018). Extended school absence can also result in hindered developmental competencies and milestones related to social functioning (Kearney and Albano, 2018).
3.3. Mental health functioning
The intricate relationship between school attendance/SAPs/absenteeism and mental health functioning has been well-documented for many decades (Kearney et al., 2019a,b). Lawrence et al. (2019) found that students with a mental disorder experienced significantly greater school absences over time, especially compared to students without a mental disorder, and that absences due to a mental disorder accounted for 13.4% of all days absent from school. The depth of this relationship is so substantial that entire subtypes of SAPs have been proposed on the basis of clinical symptom profiles. Examples include school refusal (anxiety-based absenteeism; King and Bernstein, 2001); school phobia (fear-based absenteeism; Pikulski et al., 2020); school withdrawal (parent-instigated absenteeism sometimes due to separation anxiety; Havik and Ingul, 2021); and truancy (delinquent-based absenteeism; Gerth, 2022). Diagnostic categories most commonly associated with school absenteeism, and addressed in this section, include emotional disorders, disruptive behavior disorders, substance use, developmental disorders, and traumatic disorders. Many other mental disorders (e.g., bipolar, eating, psychotic), however, have also been associated with school absenteeism (John et al., 2022).
Emotional disorders have been associated with SAPs for decades and represent one of the highest risk groups for school absenteeism (Redmond and Hosp, 2008). This grouping includes anxiety disorders, particularly generalized, social, and separation anxiety disorders, as well as anxiety-related problems such as health anxiety, obsessions and compulsions, panic attacks, perfectionism, selective mutism, somatic complaints, and specific fears (Finning et al., 2019a; John-Mora et al., 2023). This grouping also includes mood disorders and especially depression as well as self-harm behavior and suicidal ideation (Finning et al., 2019b; Epstein et al., 2020). Mechanisms for this relationship broadly include (a) school-based stimuli that provoke negative affectivity (anxiety, depression) and thus a desire to miss school, and (b) negative affectivity (whether specific to school or not) that interferes with social and academic competence, concentration, participation in school activities, performance before others, and test-taking, among other aspects, and that also interferes with school attendance (e.g., Gallé-Tessonneau et al., 2019). More specific mechanisms can include rigid thinking, overestimating the likelihood of school-based threats, low self-efficacy regarding academic work, perceived low self-competence and coping ability, sleep problems, and expectations of negative evaluation in academic and social situations (Heyne et al., 2015; Askeland et al., 2020).
Disruptive behavior disorders also commonly co-occur with school absenteeism, particularly attention-deficit/hyperactivity disorder (ADHD) and conduct disorder (Egger et al., 2003; Niemi et al., 2022). This relationship may be partly due to school-administered exclusionary discipline practices (arrest, detention, expulsion, suspension) for student misbehavior that preclude future school attendance (Wang et al., 2023). Mechanisms more specific to absenteeism and ADHD may include elevated rates of comorbid learning disorder, difficulty forming friendships, inattention, executive functioning deficits, poor parental supervision, student-teacher conflict, and peer victimization (Fleming et al., 2017; Zendarski et al., 2022). Mechanisms more specific to absenteeism and conduct disorder may include aggression toward others (including bullying), association with truant peers, rule-breaking, acute responses to threat that include school aversion, and callous-unemotional traits that can be linked to peer exclusion and poor teacher relationships, among other consequences (Fairchild et al., 2019; Levine et al., 2022). Youths with disruptive behavior disorders (as well as substance use disorders discussed next) are also at higher risk of broader-based variables such as school disengagement, lower academic attainment, and worse health outcomes (Fleming et al., 2017).
Substance use and its disorders are also deeply entwined with school absenteeism, with the literature base primarily focused on marijuana/cannabis, tobacco, alcohol, and use of multiple substances (Gakh et al., 2020). Possible mechanisms specific to this relationship include missing school to engage in substance use and other risky behaviors, less supervision/monitoring, less self-control, stress, criminality, and self-medication for emotional and sleep disorders (Kiani et al., 2018; Dennermalm et al., 2022). The relationship between substance use and school absenteeism is likely quite complex, however, intersecting with family substance use, maltreatment, race/ethnicity, poverty, interaction with the juvenile justice system, and other broader variables (Maynard et al., 2017; Iverson et al., 2018).
Developmental disorders such as intellectual developmental disorder, autism spectrum disorder, and learning disorder are also common among youths with school absenteeism (Totsika et al., 2020; Melvin et al., 2023). Youths with disabilities in general, which can include medical (next section) as well as developmental and related disorders, are a highly vulnerable group for school absenteeism (Allison et al., 2019). Youths with developmental disorders are at particular risk of being bullied and harassed at school (Bitsika et al., 2021). In addition, youths with a developmental disorder (and especially more than one) often experience chronic medical conditions, multiple psychiatric comorbidities, prolonged social isolation, inadequate resources and clinical interventions in special education placements, and parents with mental health problems, all variables that can interfere with school attendance (Black and Zablotsky, 2018; Melin et al., 2022). Youths with learning disorders are also at increased risk for SAPs, which may be impacted by avoidance of aversive academic and other school-based experiences, low self-esteem, less perceived self-efficacy in academic and social situations, and less parental control and expectations for child academic success (Filippello et al., 2020).
Traumatic disorders and experiences also intersect with SAPs in collective and individual ways. School violence and other school-based traumatic events, as well as pervasive school safety problems and victimization in general, relate closely to widespread patterns of student absenteeism (Polanin et al., 2021). At a more individual level, multiple adverse child experiences, and in particular neighborhood violence and family substance use, predict chronic absenteeism (Stempel et al., 2017). Child maltreatment intersects closely with school absenteeism for various reasons (e.g., betrayal trauma, physical injury) as well, including situations where youths are neglected or placed in residential care or where parents keep a child home from school to conceal signs of abuse (Armfield et al., 2020; Maclean et al., 2020). Traumatic experiences and their effect on school attendance (and classroom performance) may be exacerbated in certain educational settings where less efforts are devoted to detecting and addressing these issues (Blodgett and Lanigan, 2018). Mitigating effects, however, include child resilience bolstered by access to a trusted adult, community support, cultural engagement, and control of one’s personal circumstances (Bellis et al., 2018).
3.4. Physical health functioning
School absenteeism has been associated with physical health problems at both individual and epidemiological levels. At the individual level, any acute or chronic health problem can be associated with SAPs, though particularly common conditions that interfere with school attendance include asthma, allergies, dental problems, diabetes, and seizure disorders (Leroy et al., 2017). General mechanisms linking health problems with school absenteeism include anxiety, barriers to care, disease burden, complications, pain, stigma, social isolation, suboptimal symptom control, sleep problems, traumatic brain and other injury, and need for outside or emergency assistive devices, medication, or medical appointments and care (e.g., Cobham et al., 2020). More specific mechanisms include caregiver asthma, home-based mold (asthma); nasal obstruction, irritability (allergies); unmet therapeutic care, poor oral health (dental problems); elevated hemoglobin A1c, poor glucose monitoring (diabetes); and cognitive impairments and caregiver fear of sending a child to school (seizure disorders) (Hsu et al., 2016; Blaiss et al., 2018; Everhart et al., 2018; Ruff et al., 2019; Thingholm et al., 2020; Lystad et al., 2022). A connection between medical problems and school absenteeism may be further exacerbated in schools with no nurse or health center. In these settings, school officials may be more predisposed to sending home a child with physical symptoms as opposed to providing on-site care and thus minimizing school absence (Allen et al., 2018).
Youths with SAPs also commonly report somatic complaints, particularly abdominal pain, diarrhea, dizziness, fatigue, headache, heart palpitation, nausea, muscular or joint ache, and vomiting (Li et al., 2021). Somatic complaints in this population have been linked to school-based negative affectivity and sensitivity to school-based stressors, attempts to miss school due to alleged illness, and parental work absence (Havik et al., 2015; Hysing et al., 2017). Other key factors can include depression, impaired coping skills, social withdrawal, temperament, and family conflict about the symptoms (Nayak et al., 2018). A connection between somatic complaints and school absenteeism may be exacerbated as well in situations where health professionals unnecessarily excuse absences related to less severe or unexplained anxiety or somatic symptoms (Birioukov, 2016).
At the epidemiological level, school absenteeism patterns have been used as an early warning system to surveil and stem infectious diseases and other illness outbreaks (Bates, 2017). Illness-related absenteeism may be a better metric than all-cause absenteeism in this regard because youths and schools are important transmitters of infection, because school absence often occurs at the immediate onset of symptoms, because schoolchildren may easily spread illness to older family members, and because schools can serve as a central intervention point (Donaldson et al., 2021). School attendance patterns have been used to surveil conjunctivitis, coronavirus, fever, influenza, intestinal disease, and skin and other conditions (e.g., Tsang et al., 2023). School absenteeism patterns for illness surveillance may be less effective, however, than methods such as web-based apps/measures and for times of the year when school is not in session (Schellpfeffer et al., 2017).
3.5. Family functioning
School absenteeism and changes in family functioning intersect at both systemic and analytic levels. At a systemic level, researchers have examined how school absenteeism intersects with family experiences surrounding seasonal migration, immigration status, interaction with the legal system, school-based discrimination, biased disciplinary practices, and lack of access to school and community care (Kearney et al., 2023). Family residential mobility and housing insecurity also closely relate to school absenteeism patterns, especially if delays occur in new school and transportation assignments (Green et al., 2019). Many families also face barriers to regular school attendance such as difficulty securing documentation for enrollment purposes, less access to academic and technological supports and requirements, and greater exposure to health-based threats (Reimer and Hill, 2022). Many youths also depart a school campus prematurely or drop out of school permanently to support their family economically, either directly (e.g., employment) or indirectly (e.g., child care, assistance for family members with physical or mental health problems) (Chang et al., 2019).
At an analytic level, as mentioned, school absenteeism can be highly disruptive to families when caregivers must miss work, arrange alternative transportation and child care options, attend school-based conferences, absorb financial costs, and experience other daily disturbances (Marchbanks et al., 2014). Child behavior problems associated with school absenteeism can also be disrupting, taxing, stigmatizing, and stressful for family members (Maynard et al., 2018). Family disruption can further erode productive communications with school officials regarding policies, causal events, intervention directions, accommodation plans, and other aspects related to absenteeism (Havik et al., 2014). In addition, often in conjunction with such disruption, school absenteeism is related to problematic parenting behaviors such as inaction, ineffective response strategies, overprotectiveness, vague commands, aggression, and reinforcement of attention-seeking behavior (Kearney, 2019; Chockalingam et al., 2022). Low parenting self-efficacy can be a key mechanism in families of youths with SAPs (Carless et al., 2015).
School absenteeism can also be central to significant, maladaptive changes in family dynamics. A primary example is greater family conflict regarding stressors from, and disagreements about how to resolve, SAPs (Sosu et al., 2021). Family conflict may increase as school absenteeism severity becomes moderate and may actually decline with greater absenteeism severity once family members acquiesce and withdraw from problem-solving efforts (Fornander and Kearney, 2019). In addition, family conflict may be greater in situations where a child actively rejects school as opposed to having difficulties attending school due to emotional problems (Gonzálvez et al., 2019b). Family as well as marital conflict can impair communication and problem-solving abilities and reduce a student’s motivation to return to school (Ingul et al., 2019). Such conflict can produce other situations that may exacerbate school absenteeism as well, such as maltreatment, substance use, trauma, and other adverse child experiences (Duke, 2020).
Other key changes in family dynamics related to school absenteeism include enmeshment, detachment, and isolation. Enmeshment surrounds overinvolvement of family members into each other’s lives with greater emotional dependency and less autonomy (Berryhill et al., 2018). Enmeshment could be manifested by parents attending school with their child, advocating for unrealistic school-based accommodations, or unnecessarily rescuing a child from anxiety-provoking situations. Detachment refers to under-involvement of family members into each other’s lives, including greater caregiver and child passivity (Lindblom et al., 2017). Detachment could be manifested by lack of response to school official notices and appeals, less supervision and academic involvement, and failure to intervene early in an absenteeism resolution process. Other families are quite isolated from outside systems and have little contact with external agencies or teachers and other school officials (Tucker and Rodriguez, 2014). Families of youths with SAPs have also been found to have lower levels of achievement orientation, active-recreational orientation, cohesion, and expressiveness (Hansen et al., 1998; Fornander and Kearney, 2019).
3.6. School and community functioning
School absenteeism rates are also sometimes a warning signal for changes that can rapidly affect the K-12 educational system, including shifts in school funding, school closures, and truancy and legal policies. Elevated school absenteeism and dropout rates can cause schools to lose funding and force a shift from costlier rehabilitative to less costly punitive paradigms for school attendance and other problems (Mallett, 2016). Diminished school enrollment and lower standardized test scores can also produce rapid school closures, particularly in impoverished neighborhoods, which can further accelerate academic decline if transfers to new transportation routes and schools are delayed or onerous (Kirshner et al., 2016). In addition, truancy, and other legal policies, including punitive zero-tolerance laws, are often enacted to compel attendance in response to high absenteeism rates. Such policies, however, generally ignore more systemic problems outside a family’s control, are applied disproportionately to minority groups, and paradoxically increase barriers to future school attendance (Conry and Richards, 2018).
School absenteeism rates can also signal problems in school climate, or the quality and character of daily school life (Thapa and Cohen, 2017). Dimensions of school climate include relationships (social relationships, school connectedness, leadership, culture); environment (school facilities, physical comfort, cleanliness); safety/discipline (school safety, fairness of rules, bullying and aggression, disciplinary harshness, drug use); and academic (academic outcomes, equality of opportunity, engagement, cohesiveness/competitiveness) (Gonzálvez et al., 2023). Studies reveal a general intersection of school absenteeism rates with perceived negative school climate (Van Eck et al., 2017). More specifically, school absenteeism rates are inversely associated with positive school climate variables such as academic engagement, order and discipline, parental involvement, personal connectedness, school safety, school satisfaction, student access to resources, student interpersonal relations, and student–teacher relations (Hendron and Kearney, 2016; Daily et al., 2020; Hamlin, 2021). These effects are pronounced for vulnerable student groups and particularly for those who experience a school setting as oppressive (Kohli et al., 2017). Potential mechanisms relevant here include prejudicial and discriminatory treatment of student groups by school officials, assignment to less rigorous courses, greater exposure to harassment based on protected status, student-faculty ethnicity mismatch, and teacher qualification gaps (Kutsyuruba et al., 2015).
Widespread patterns of school absenteeism in certain geographical communities have also been used to identify unhealthy economic, political, and other exosystem/macrosystem states and policies that produce inequitable resources and practices and disproportionate outcomes (Lenhoff et al., 2022). Root cause analyses of areas with high chronic school absenteeism have uncovered key sources such as inadequate transportation, housing dilapidation, high use of emergency services, unsafe avenues to school, and food insecurity, among others (Kearney and Childs, 2023). Others contend that high rates of unexcused absences are a signal for crises and challenges faced by students and families outside of school (Pyne et al., 2023). School absenteeism and dropout rates can also reflect economic push and pull factors, as when students leave school to assist a family or to pursue available and lucrative employment opportunities that do not require high school completion (McDermott et al., 2018). School absenteeism and dropout rates intersect as well with shifts in labor, demographic, technological, climate, and immigration/migration patterns (Brewer and McEwan, 2010; Kearney et al., 2022).
School absenteeism rates have also been used as a key element of early warning systems to predict later school dropout, often in conjunction with school disengagement and behavior problems (Balfanz and Byrnes, 2019). More nuanced early warning systems and algorithms, however, include nonacademic variables, are better tailored to a particular community, consider intersectionality of influencing factors, and examine specific mechanisms within pathways. Examples of identified nonacademic variables include marital status, family structure, and medical restrictions (Chu et al., 2019; Jarbou et al., 2022). Analyses tailored to a particular community can reveal pertinent local conditions such as elevated rates of homelessness, pregnancy, and substance use (US Department of Education, 2016). Machine learning and data mining techniques are also helpful for simultaneously examining multiple and intersecting (e.g., disability × socioeconomic status) factors to pinpoint more precise outcomes for different student groups (Newman et al., 2019). Specific mechanisms to explain pathways to certain outcomes can be identified as well. Hancock et al. (2017), for example, investigated the oft-cited relationship between school absenteeism and community poverty via multilevel modeling to identify mechanisms such as degree of access to learning activities, availability of high-quality teachers, opportunities to complete missed academic work, and parent-school faculty language differences.
4. Implications for health-based protocols and school-based practices
Changes in school attendance are deeply embedded with changes in functioning in many domains and may thus be a particularly sensitive signal that reflects system instability and that could indicate (sometimes sudden) movement from a healthy to an unhealthy state for students, families, schools, and communities. Changes in school attendance have several key advantages as a potential signal: they are typically recognizable, fluid, dynamic, applicable to most students and families, easily measured daily, and represent a clear intervention target and outcome variable. As such, a focus on changes in school attendance as a signal of impaired functioning across many domains has several implications for health-based protocols (enhancing access to care; integrating systems of care) and school-based practices (developing multi-tiered systems of support and community asset maps; modifying educational and policy perspectives). These implications are discussed in the next sections.
4.1. Enhancing access to care
A view of changes in school attendance as a key signal of system instability has implications for enhancing access to health-based care for students and their families. A substantial gap exists between the prevalence of child health disorders and accessed services for these disorders (Wainberg et al., 2017). This gap and its causes (barriers) are especially pertinent to SAPs for several reasons. First, many youths experience changes in school attendance but caregivers and health professionals are often unsure as to whether these changes are transient and normal or are problems in need of intervention (Kazdin, 2019). Second, families and others that seek community-based care for SAPs often face fragmented service delivery systems in part due to confusion about whether SAPs lie more within educational, medical, mental health, or family or other intervention realms (Radez et al., 2021). Third, common structural barriers especially pertinent to SAPs include inaccessible and too few providers, cost, transportation challenges, stigma, long wait times, lack of insurance, cultural and language differences, and lack of provider knowledge about SAPs (Kearney, 2019; Tambling et al., 2021). Substantial barriers also impede effective home-school collaborations to address SAPs, including time poverty, negative interactions with school personnel, and lack of awareness about the issue (Williams and Sánchez, 2011).
A view of school attendance changes as a key signal of system instability (and unhealthy status) means that caregivers and health professionals would benefit from guidelines to quickly determine if such changes are innocuous or in need of intervention because several domains of functioning risk impairment. Kearney et al. (2022) outlined functional impairment guidelines for SAPs across school, social, and family domains. The school domain included timing of absences (greater impairment from absences early in a school year, during critical evaluation periods, and in preschool or high-impact grade levels); interference with academic competence; and whether absences trigger an administrative or legal action that impedes future school attendance. The social domain included interference with social competence; interference in interpersonal relationships; and enhanced risk of harm to others. The family domain included interference with daily family functioning; significant and maladaptive changes in family dynamics; and substantial cost to family members. Measures of functional impairment relevant to SAPs would enhance cost-effective screening and rapid clinical decision-making processes and may also signal other problems such as physical or mental health disorders.
Treatment barriers with respect to SAPs could be partly addressed by integrating service delivery systems within schools (later section) and by leveraging novel modes of service delivery when even subtle changes occur in school attendance. New digital modes of delivery for mental health intervention include games and computer-assisted programs, mobile text messaging, portals, robots, smartphone (and other digital device) applications, telehealth videoconferencing, virtual reality, and wearable devices, among others (Hollis et al., 2017). Key elements of these delivery modes include peer-to-peer communication, reminders, skills development, social networking, and therapeutic and emotional regulation support (Liverpool et al., 2020). Digital device applications, portals, and text messaging can be used to inform parents of absences in real time and allow absent students to upload academic work to minimize impairment (Smythe-Leistico and Page, 2018). Other novel modes of service delivery could be expanded to better reach absent students at home, incorporate school attendance assessment and intervention techniques, provide mentoring, surveil developmental problems, and distribute a community asset map of available supports (later section).
4.2. Integrating systems of care
Another barrier to care especially pertinent to SAPs is lack of surveillance across systems of care regarding youths separated from the educational process (Kazdin, 2019). Many families of youths with SAPs, and particularly those with comorbid health problems and/or disabilities, must navigate several different systems (educational, medical, mental health, legal, developmental) to access appropriate or available care or to meet obligations accrued as a result of the SAPs (Gottfried et al., 2019; Kearney and Benoit, 2022). Many youths also transition between these systems, which disrupts their ability to attend school. Youths in child protective services and juvenile justice agencies are at particularly high risk of chronic absenteeism (Yoon et al., 2021). SAPs are also commonly aggravated by family exigent circumstances that require seeking survival resources across multiple agencies (e.g., employment, housing, nutrition) (Sugrue et al., 2016).
These disparate systems of care are often quite disconnected, particularly with respect to sharing information relevant to student location and school attendance (Balfanz and Byrnes, 2013; Klein et al., 2020). Sokol et al. (2019) reviewed screening tools for social determinants of health in children across various care agencies, finding that variables assessed in the education domain included lack of child care, degree of parental education, and concerns about a child’s learning or behavior at school. None included school attendance or absenteeism. This is unfortunate given that school absenteeism is often viewed as a wicked problem in need of complex, coordinated interventions (Childs and Lofton, 2021). Researchers and others have thus emphasized the need for shared alliances or integration across agencies that address families with various needs and especially with respect to SAPs (Balfanz and Byrnes, 2018). Key aspects of these shared alliances include multiagency tracking of students using interoperable metrics as well as coordinated intervention across systems to address the needs of particularly vulnerable students with SAPs.
A primary goal of multiagency tracking of students involves collaboration among various agencies to identify students separated from the educational process and to facilitate their reintegration to an appropriate school completion pathway. A secondary goal of such tracking is to identify key drivers of school absenteeism in a given community, such as high rates of housing/food insecurity or foster care, that lead to lengthy school displacements for students (Richardson et al., 2018). Such tracking should include sharing early warning signal data that is best interoperable across agencies, such as residential status, family contact with various entities, and school assignment, placement, enrollment, attendance, transportation, and impairment data (Welsh, 2018). Data provided in real time via attendance dashboards could be shared across school districts, state and service agencies, and relevant community partners (Chang and Balfanz, 2016). Recent advances in data mining and algorithmic modeling also provide the means to examine disaggregated data to better inform pertinent local components of attendance dashboards as well as websites for information dissemination and psychoeducation regarding SAPs (Boustani et al., 2020; Kearney and Childs, 2023).
Shared alliances would also benefit the most vulnerable of students with respect to school attendance, particularly those with chronic physical/mental health conditions, academic and developmental problems, and family disarray and lack of resources. Such alliances could include representatives from various systems of care that are based in a school setting to reduce access, stigma, transportation, cost, and other concerns (Lewallen et al., 2015). These alliances would review longstanding absenteeism cases, incubate family-school-community partnerships to boost wraparound care, and institute necessary case management and accommodation practices to address salient learning, health, and behavioral issues that interfere with school attendance (Cumming et al., 2022). These alliances can be linked as well to school-based teams that utilize attendance data as an early warning signal for multiple issues (next section).
4.3. Developing multi-tiered systems of support and community asset maps
Recall that access to care barriers with respect to SAPs could also be addressed by integrating service delivery systems within a school system, which is the current source of mental health and other nonacademic care for most children and adolescents (Duong et al., 2021). An emerging service delivery model within schools involves multi-tiered systems of support (MTSS) to provide various levels of student support based on prescribed need (Stoiber and Gettinger, 2016). MTSS models are typically arranged into three tiers that include universal intervention for preventative purposes (Tier 1); early and less complex interventions for emerging or acute problems (Tier 2); and later and more complex interventions for chronic and severe problems. MTSS models have been designed for many academic, social, and behavioral challenges and have been more recently extended to the arrangement of interventions for SAPs per se (Kearney and Graczyk, 2014, 2020, 2022).
Proposed MTSS models for various issues often rely on school attendance data as a key early warning signal of problems that require additional levels of support (Freeman et al., 2016). Tier 1 assessment methods in this regard include screening for various kinds of SAPs (e.g., complete and partial absences), important precursors to SAPs such as sudden changes in academic work, and reasons and categories for missing school (e.g., illness, unexcused) (Kearney, 2016). School attendance data should be supplemented with screening instruments for academic, behavioral, emotional, and social functioning as well as for important contextual variables across different ecological levels to determine personalized Tier 2 supports (Kearney and Childs, 2022). An example includes a student with short-term absenteeism and academic and mental health challenges whose family changes residence during the academic year and thus requires additional transitional support. School attendance and supplemental data can also be used to inform decisions about appropriate Tier 3 services, particularly for cases involving extensive contextual influences (Hobbs et al., 2018). An example includes a student with long-term absenteeism and disability coupled with family dysfunction, peer victimization, and exclusionary discipline who requires an innovative plan to achieve school completion.
Key advantages of utilizing attendance data as an early warning signal in a school-based MTSS approach are that the data are usually comparable across educational districts and subject to cost-effective tracking software and mobile applications (Pangrazio et al., 2023). Frequent attendance data review also allows schools to eschew a “wait to fail” approach that long delays intervention until a legal tripwire is triggered for unexcused absences (Conry and Richards, 2018). Absenteeism rates can also signal school climate problems and community barriers to school attendance. In addition, an MTSS approach based on attendance data review can facilitate task sharing among in-house personnel to streamline intervention (Raviola et al., 2019). This could involve a school attendance team that reviews data and implements same-day intervention for any school absence, an applied school support team (e.g., school-based counselor, nurse, psychologist, social worker) that implements interventions for Tier 2 cases, and a school administrative team (e.g., principal, vice principal, dean) that coordinates referrals for more advanced Tier 3 cases (Kearney, 2016).
An important adjunct of MTSS models, especially for SAPs, is a community asset map of internal school-based and external community-based resources to identify functional domain support options, particularly for highly vulnerable students such as those with disabilities (Bryan et al., 2020). Such maps could help streamline care by supplying front-line personnel such as school counselors with real-time data about immediately available and appropriate support options. Examples of internal resources include alternative educational schools and pathways, second chance programs, mentoring opportunities, adult readiness initiatives, and truancy diversion and other restorative efforts to boost academic enrichment and enhance school completion (Todić et al., 2020). Examples of external resources can include health professionals, businesses, and private and public legal, developmental, and social services agencies to provide supports necessary to facilitate school attendance (Mitchell et al., 2019). Self-assessment resources are available for schools to map existing supports and develop additional supports specific to SAPs (Attendance Works, 2023).
4.4. Modifying educational and policy perspectives
A view of changes in school attendance as a warning signal also has ramifications for contemporary K-12 educational and policy perspectives regarding SAPs and their associated (e.g., mental health, school climate) effects. The current dominant educational/policy perspective of school absenteeism emphasizes parental responsibility, compulsory school attendance until a set age, and various punitive and exclusionary administrative and legal consequences for days missed from school (Reyes, 2020). This perspective typically centers around deficit narratives that place much of the blame, stigma, and onus for resolving SAPs on students and families, even in circumstances beyond their control (Grooms and Bohorquez, 2022). Unexcused absences in particular are often associated with willful, deliberate, and deviant student behavior (Birioukov, 2016). The current dominant educational/policy perspective diminishes the complex nature of school absenteeism, often ignoring community-based and other broader root causes of SAPs, constrains student/family agency and access to needed supports, and limits opportunities for school completion (Childs and Lofton, 2021).
A focus on changes in school attendance as a warning signal may be the premise for modifying educational and policy perspectives away from simple absenteeism deterrence and toward holistic attendance enhancement (Gentle-Genitty et al., 2020). A holistic approach in this regard would include greater emphasis on Tier 1 strategies shown to boost attendance rates, including existing school-based strategies to improve physical and mental health, social–emotional learning, safety, and climate (Langford et al., 2015). More specifically, a holistic approach would involve universal and frequent screening of even subtle attendance changes, qualitative methods to understand relevant contextual factors, assignment of focused and restorative interventions, and partnerships with community entities for empowering supports (Kim and Gentle-Genitty, 2020; Kipp and Clark, 2022). A more holistic approach toward improving attendance would also include an understanding that the surrounding community must often be an additional and sometimes primary target of intervention (Kearney and Graczyk, 2022). In essence, systems themselves must send their own signal that school attendance at any level is valued, appreciated, important, and prioritized (Warne et al., 2020).
5. Conclusion
School attendance problems/absenteeism are often examined statically as a health-based syndrome or as a school-based condition that requires remediation. An alternative approach is to view changes in school attendance more dynamically as early warning signals of potentially unhealthy functioning in multiple ecological domains. This approach dovetails with recent efforts to view school attendance problems more dimensionally and flexibly with respect to definition, demarcation, subtyping, risk and protective factors, interventions, and school completion (Kearney and Gonzálvez, 2022). In addition, viewing changes in school attendance as early warning signals can help health-and school-based professionals maximally leverage innovative and radical developments in service delivery options for youths (Kruk et al., 2022). At the same time, such an approach enhances one of the most fundamental endeavors of childhood that maintains healthy societal functioning: education.
Author contributions
CK: Writing – original draft, Writing – review & editing. RD: Writing – original draft, Writing – review & editing. MF: Writing – original draft, Writing – review & editing. CG: Writing – original draft, Writing – review & editing.
Acknowledgments
The authors express gratitude to the University of Nevada, Las Vegas and to the University of Alicante.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Allen, C. W., Diamond-Myrsten, S., and Rollins, L. K. (2018). School absenteeism in children and adolescents. Am. Fam. Physician 98, 738–744.
Allison, M. A., Attisha, E., Lerner, M., De Pinto, C. D., Beers, N. S., Gibson, E. J., et al. (2019). The link between school attendance and good health. Pediatrics 143:e20183648. doi: 10.1542/peds.2018-3648
Ansari, A., and Gottfried, M. A. (2021). The grade-level and cumulative outcomes of absenteeism. Child Dev. 92, e548–e564. doi: 10.1111/cdev.13555
Ansari, A., Hofkens, T. L., and Pianta, R. C. (2020). Absenteeism in the first decade of education forecasts civic engagement and educational and socioeconomic prospects in young adulthood. J. Youth Adolesc. 49, 1835–1848. doi: 10.1007/s10964-020-01272-4
Ansari, A., and Pianta, R. C. (2019). School absenteeism in the first decade of education and outcomes in adolescence. J. Sch. Psychol. 76, 48–61. doi: 10.1016/j.jsp.2019.07.010
Appleton, J. J., Christenson, S. L., Kim, D., and Reschly, A. L. (2006). Measuring cognitive and psychological engagement: validation of the student engagement instrument. J. Sch. Psychol. 44, 427–445. doi: 10.1016/j.jsp.2006.04.002
Archambault, I., Janosz, M., Olivier, E., and Dupéré, V. (2022). “Student engagement and school dropout: theories, evidence, and future directions” in Handbook of research on student engagement. eds. A. L. Reschly and S. L. Christenson. 2nd Edn. (Cham: Springer), 331–355.
Armfield, J. M., Gnanamanickam, E., Nguyen, H. T., Doidge, J. C., Brown, D. S., Preen, D. B., et al. (2020). School absenteeism associated with child protection system involvement, maltreatment type, and time in out-of-home care. Child Maltreat. 25, 433–445. doi: 10.1177/1077559520907682
Askeland, K. G., Bøe, T., Lundervold, A. J., Stormark, K. M., and Hysing, M. (2020). The association between symptoms of depression and school absence in a population-based study of late adolescents. Front. Psychol. 11:1268. doi: 10.3389/fpsyg.2020.01268
Attendance Works. (2023). Self-assessments. Available at: https://www.attendanceworks.org/resources/self-assessment/ (Accessed April 7, 2023).
Aucejo, E. M., and Romano, T. F. (2016). Assessing the effect of school days and absences on test score performance. Econ. Educ. Rev. 55, 70–87. doi: 10.1016/j.econedurev.2016.08.007
Bacon, V. R., and Kearney, C. A. (2020). School climate and student-based contextual learning factors as predictors of school absenteeism severity at multiple levels via CHAID analysis. Child Youth Serv. Rev. 118, 1–9. doi: 10.1016/j.childyouth.2020.105452
Baiden, P., LaBrenz, C. A., Okine, L., Thrasher, S., and Asiedua-Baiden, G. (2020). The toxic duo: bullying involvement and adverse childhood experiences as factors associated with school disengagement among children. Child Youth Serv. Rev. 119:105383. doi: 10.1016/j.childyouth.2020.105383
Balfanz, R., and Byrnes, V. (2013). Meeting the challenge of combating chronic absenteeism. Baltimore, MD: Johns Hopkins University School of Education.
Balfanz, R., and Byrnes, V. (2018). Using data and the human touch: evaluating the NYC inter-agency campaign to reduce chronic absenteeism. J. Educ. Stud. Placed Risk 23, 107–121. doi: 10.1080/10824669.2018.1435283
Balfanz, R., and Byrnes, V. (2019). “Early warning indicators and early intervention systems: state of the field” in Handbook of student engagement interventions: Working with disengaged students. eds. J. A. Fredricks, A. L. Reschly, and S. L. Christenson (New York, NY: Elsevier), 45–56.
Bates, M. (2017). Tracking disease: digital epidemiology offers new promise in predicting outbreaks. IEEE Pulse 8, 18–22. doi: 10.1109/MPUL.2016.2627238
Bellis, M. A., Hughes, K., Ford, K., Hardcastle, K. A., Sharp, C. A., Wood, S., et al. (2018). Adverse childhood experiences and sources of childhood resilience: a retrospective study of their combined relationships with child health and educational attendance. BMC Public Health 18:792. doi: 10.1186/s12889-018-5699-8
Ben-Eliyahu, A., Moore, D., Dorph, R., and Schunn, C. D. (2018). Investigating the multidimensionality of engagement: affective, behavioral, and cognitive engagement across science activities and contexts. Contemp. Educ. Psychol. 53, 87–105. doi: 10.1016/j.cedpsych.2018.01.002
Berkowitz, R., Moore, H., Astor, R. A., and Benbenishty, R. (2017). A research synthesis of the associations between socioeconomic background, inequality, school climate, and academic achievement. Rev. Educ. Res. 87, 425–469. doi: 10.3102/0034654316669821
Berryhill, M. B., Hayes, A., and Lloyd, K. (2018). Chaotic-enmeshment and anxiety: the mediating role of psychological flexibility and self-compassion. Contemp. Fam. Ther. 40, 326–337. doi: 10.1007/s10591-018-9461-2
Bettis, A. H., Burke, T. A., Nesi, J., and Liu, R. T. (2022). Digital technologies for emotion-regulation assessment and intervention: a conceptual review. Clin. Psychol. Sci. 10, 3–26. doi: 10.1177/21677026211011982
Birioukov, A. (2016). Beyond the excused/unexcused absence binary: classifying absenteeism through a voluntary/involuntary absence framework. Edu. Rev. 68, 340–357. doi: 10.1080/00131911.2015.1090400
Bitsika, V., Heyne, D. A., and Sharpley, C. F. (2021). Is bullying associated with emerging school refusal in autistic boys? J. Autism Dev. Disord. 51, 1081–1092. doi: 10.1007/s10803-020-04610-4
Black, L. I., and Zablotsky, B. (2018). Chronic school absenteeism among children with selected developmental disabilities: National Health Interview Survey, 2014–2016. National Health Statistics Reports. Number 118. Hyattsville, MD: National Center for Health Statistics.
Blaiss, M. S., Hammerby, E., Robinson, S., Kennedy-Martin, T., and Buchs, S. (2018). The burden of allergic rhinitis and allergic rhinoconjunctivitis on adolescents: a literature review. Ann. Allergy Asthma Immunol. 121, 43–52. doi: 10.1016/j.anai.2018.03.028
Blodgett, C., and Lanigan, J. D. (2018). The association between adverse childhood experience (ACE) and school success in elementary school children. Sch. Psychol. Q. 33, 137–146. doi: 10.1037/spq0000256
Boustani, M. M., Frazier, S. L., Chu, W., Lesperance, N., Becker, K. D., Helseth, S. A., et al. (2020). Common elements of childhood universal mental health programming. Admin. Pol. Ment. Health 47, 475–486. doi: 10.1007/s10488-020-01023-4
Bryan, J., Williams, J. M., and Griffin, D. (2020). Fostering educational resilience and opportunities in urban schools through equity-focused school–family–community partnerships. Prof. Sch. Couns. 23, 1–14. doi: 10.1177/2156759X19899179
Calderón-Larrañaga, A., Vetrano, D. L., Ferrucci, L., Mercer, S. W., Marengoni, A., Onder, G., et al. (2019). Multimorbidity and functional impairment–bidirectional interplay, synergistic effects and common pathways. J. Intern. Med. 285, 255–271. doi: 10.1111/joim.12843
Carless, B., Melvin, G. A., Tonge, B. J., and Newman, L. K. (2015). The role of parental self-efficacy in adolescent school-refusal. J. Fam. Psychol. 29, 162–170. doi: 10.1037/fam0000050
Carpentieri, R., Iannoni, M. E., Curto, M., Biagiarelli, M., Listanti, G., Andraos, M. P., et al. (2022). School refusal behavior: role of personality styles, social functioning, and psychiatric symptoms in a sample of adolescent help-seekers. Clin. Neuropsychiatry 19, 20–28. doi: 10.36131/cnfioritieditore20220104
Carroll, E., McCoy, S., and Mihut, G. (2022). Exploring cumulative disadvantage in early school leaving and planned post-school pathways among those identified with special educational needs in Irish primary schools. Br. Educ. Res. J. 48, 1065–1082. doi: 10.1002/berj.3815
Chang, H., and Balfanz, R. (2016). Preventing missed opportunity: Taking collective action to confront chronic absence. San Francisco: Everyone Graduates Center/Attendance Works.
Chang, H. N., Osher, D., Schanfield, M., Sundius, J., and Bauer, L. (2019). Using chronic absence data to improve conditions for learning. San Francisco, CA: Attendance Works and American Institutes for Research.
Childs, J., and Lofton, R. (2021). Masking attendance: how education policy distracts from the wicked problem(s) of chronic absenteeism. Educ. Policy 35, 213–234. doi: 10.1177/0895904820986771
Chockalingam, M., Skinner, K., Melvin, G., and Yap, M. B. (2022). Modifiable parent factors associated with child and adolescent school refusal: a systematic review. Child Psychiatry Hum. Dev. 54, 1459–1475. doi: 10.1007/s10578-022-01358-z
Chu, B. C., Guarino, D., Mele, C., O’Connell, J., and Coto, P. (2019). Developing an online early detection system for school attendance problems: results from a research-community partnership. Cogn. Behav. Pract. 26, 35–45. doi: 10.1016/j.cbpra.2018.09.001
Chu, B. C., Rizvi, S. L., Zendegui, E. A., and Bonavitacola, L. (2015). Dialectical behavior therapy for school refusal: treatment development and incorporation of web-based coaching. Cogn. Behav. Pract. 22, 317–330. doi: 10.1016/j.cbpra.2014.08.002
Classi, P., Milton, D., Ward, S., Sarsour, K., and Johnston, J. (2012). Social and emotional difficulties in children with ADHD and the impact on school attendance and healthcare utilization. Child Adolesc. Psychiatry Ment. Health 6, 1–8. doi: 10.1186/1753-2000-6-33
Clements, C. F., McCarthy, M. A., and Blanchard, J. L. (2019). Early warning signals of recovery in complex systems. Nat. Commun. 10:1681. doi: 10.1038/s41467-019-09684-y
Cobham, V. E., Hickling, A., Kimball, H., Thomas, H. J., Scott, J. G., and Middeldorp, C. M. (2020). Systematic review: anxiety in children and adolescents with chronic medical conditions. J. Am. Acad. Child Adolesc. Psychiatry 59, 595–618. doi: 10.1016/j.jaac.2019.10.010
Conry, J. M., and Richards, M. P. (2018). The severity of state truancy policies and chronic absenteeism. J. Educ. Stud. Placed Risk 23, 187–203. doi: 10.1080/10824669.2018.1439752
Cumming, T., Strnadová, I., Lee, H., and Lonergan, R. (2022). Education-centred formal wraparound services in support of school-aged students with complex support needs: a systematic review. Australasian J. Spec. Inclusive Educ. 46, 47–60. doi: 10.1017/jsi.2022.1
Daily, S. M., Smith, M. L., Lilly, C. L., Davidov, D. M., Mann, M. J., and Kristjansson, A. L. (2020). Using school climate to improve attendance and grades: understanding the importance of school satisfaction among middle and high school students. J. Sch. Health 90, 683–693. doi: 10.1111/josh.12929
Darling-Hammond, L., Bae, S., Cook-Harvey, C. M., Lam, L., Mercer, C., Podolsky, A., et al. (2016). Pathways to new accountability through the every student succeeds act. Palo Alto: Learning Policy Institute.
Darling-Hammond, L., and Cook-Harvey, C. M. (2018). Educating the whole child: Improving school climate to support student success. Palo Alto, CA: Learning Policy Institute.
de Groot, C. M., Heyne, D., and Boon, A. E. (2023). School refusal in adolescence: personality traits and their influence on treatment outcome. J. Emot. Behav. Disord. doi: 10.1177/106342662311519
de Lijster, J. M., Dieleman, G. C., Utens, E. M., Dierckx, B., Wierenga, M., Verhulst, F. C., et al. (2018). Social and academic functioning in adolescents with anxiety disorders: a systematic review. J. Affect. Disord. 230, 108–117. doi: 10.1016/j.jad.2018.01.008
Dennermalm, N., Karlsson, P., and Ekendahl, M. (2022). Risk factors for substance use in Swedish adolescents: a study across substances and time points. Nordisk Alkohol. Nark. 39, 535–552. doi: 10.1177/14550725221108792
DePaoli, J. L., Balfanz, R., Atwell, M. N., and Bridgeland, J. (2018). Building a grad nation: Progress and challenge in raising high school graduation rates. Baltimore, MD: Civic Enterprises.
Donaldson, A. L., Hardstaff, J. L., Harris, J. P., Vivancos, R., and O’Brien, S. J. (2021). School-based surveillance of acute infectious disease in children: a systematic review. BMC Infect. Dis. 21, 1–10. doi: 10.1186/s12879-021-06444-6
Duke, N. N. (2020). Adolescent adversity, school attendance and academic achievement: school connection and the potential for mitigating risk. J. Sch. Health 90, 618–629. doi: 10.1111/josh.12910
Duong, M. T., Bruns, E. J., Lee, K., Cox, S., Coifman, J., Mayworm, A., et al. (2021). Rates of mental health service utilization by children and adolescents in schools and other common service settings: a systematic review and meta-analysis. Admin. Pol. Ment. Health 48, 420–439. doi: 10.1007/s10488-020-01080-9
Egger, H. L., Costello, J. E., and Angold, A. (2003). School refusal and psychiatric disorders: a community study. J. Am. Acad. Child Adolesc. Psychiatry 42, 797–807. doi: 10.1097/01.CHI.0000046865.56865.79
Epstein, S., Roberts, E., Sedgwick, R., Polling, C., Finning, K., Ford, T., et al. (2020). School absenteeism as a risk factor for self-harm and suicidal ideation in children and adolescents: a systematic review and meta-analysis. Eur. Child Adolesc. Psychiatry 29, 1175–1194. doi: 10.1007/s00787-019-01327-3
Everhart, R. S., Miller, S., Leibach, G. G., Dahl, A. L., and Koinis-Mitchell, D. (2018). Caregiver asthma in urban families: implications for school absenteeism. J. Sch. Nurs. 34, 108–113. doi: 10.1177/1059840516689326
Fairchild, G., Hawes, D. J., Frick, P. J., Copeland, W. E., Odgers, C. L., Franke, B., et al. (2019). Conduct disorder. Nat. Rev. Dis. Primers. 5:43. doi: 10.1038/s41572-019-0095-y
Filippello, P., Buzzai, C., Messina, G., Mafodda, A. V., and Sorrenti, L. (2020). School refusal in students with low academic performances and specific learning disorder. The role of self-esteem and perceived parental psychological control. Intl. J. Disabil. Dev. Educ. 67, 592–607. doi: 10.1080/1034912X.2019.1626006
Filippello, P., Sorrenti, L., Buzzai, C., and Costa, S. (2018). Predicting risk of school refusal: examining the incremental role of trait EI beyond personality and emotion regulation. Psihologija 51, 51–67.
Finning, K., Ukoumunne, O. C., Ford, T., Danielson-Waters, E., Shaw, L., Romero De Jager, I., et al. (2019a). The association between anxiety and poor attendance at school–a systematic review. Child Adolesc. Psychiatry Ment. Health 24, 205–216. doi: 10.1111/camh.12322
Finning, K., Ukoumunne, O. C., Ford, T., Danielsson-Waters, E., Shaw, L., Romero De Jager, I., et al. (2019b). The association between child and adolescent depression and poor attendance at school: a systematic review and meta-analysis. J. Affect. Disord. 245, 928–938. doi: 10.1016/j.jad.2018.11.055
Fleming, M., Fitton, C. A., Steiner, M. F., McLay, J. S., Clark, D., King, A., et al. (2017). Educational and health outcomes of children treated for attention-deficit/hyperactivity disorder. JAMA Pediatr. 171:e170691. doi: 10.1001/jamapediatrics.2017.0691
Fornander, M., and Kearney, C. A. (2019). Family environment variables as predictors of school absenteeism severity at multiple levels: ensemble and classification and regression tree analysis. Front. Psychol. 10:2381. doi: 10.3389/fpsyg.2019.02381
Fraccascia, L., Giannoccaro, I., and Albino, V. (2018). Resilience of complex systems: state of the art and directions for future research. Complexity 2018:44. doi: 10.1155/2018/3421529
Fredricks, J. A., Blumenfeld, P. C., and Paris, A. H. (2004). School engagement: potential of the concept, state of the evidence. Rev. Educ. Res. 74, 59–109. doi: 10.3102/0034654307400105
Freeman, J., Simonsen, B., McCoach, D. B., Sugai, G., Lombardi, A., and Horner, R. (2016). Relationship between school-wide positive behavior interventions and supports and academic, attendance, and behavior outcomes in high schools. J. Posit. Behav. Interv. 18, 41–51. doi: 10.1177/1098300715580992
Fuhs, M. W., Nesbitt, K. T., and Jackson, H. (2018). Chronic absenteeism and preschool children's executive functioning skills development. J. Educ. Stud. Placed Risk 23, 39–52. doi: 10.1080/10824669.2018.1438201
Gakh, M., Coughenour, C., Assoumou, B. O., and Vanderstelt, M. (2020). The relationship between school absenteeism and substance use: an integrative literature review. Subst. Use Misuse 55, 491–502. doi: 10.1080/10826084.2019.1686021
Gallé-Tessonneau, M., Johnsen, D. B., and Keppens, G. (2019). The relationship between mental health and school absenteeism in a community sample of French secondary school students: four profiles derived from cluster analysis. Eur. J. Educ. Psychol. 12, 77–90. doi: 10.30552/ejep.v12i1.242
Gentle-Genitty, C., Taylor, J., and Renguette, C. (2020). A change in the frame: from absenteeism to attendance. Front. Educ. 4:161. doi: 10.3389/feduc.2019.00161
Gershenson, S., Jacknowitz, A., and Brannegan, A. (2017). Are student absences worth the worry in US primary schools? Educ. Finance Policy 12, 137–165. doi: 10.1162/EDFP_a_00207
Gerth, M. (2022). Does truancy make the delinquent? A situational and longitudinal analysis of the truancy-delinquency relationship. Eur. J. Criminol. 19, 1205–1224. doi: 10.1177/1477370820952681
Gonzálvez, C., Bacon, V., and Kearney, C. A. (2023). Systematic and evaluative review of school climate instruments for students, teachers, and parents. Psychol. Sch. 60, 1781–1836. doi: 10.1002/pits.22838
Gonzálvez, C., Diaz-Herrero, A., Sanmartín, R., Vicent, M., Perez-Sanchez, A. M., and García-Fernández, J. M. (2019b). Identifying risk profiles of school refusal behavior: differences in social anxiety and family functioning among Spanish adolescents. Int. J. Environ. Res. Public Health 16:3731. doi: 10.3390/ijerph16193731
Gonzálvez, C., Inglés, C. J., Kearney, C. A., Sanmartín, R., Vicent, M., and García-Fernández, J. M. (2019a). Relationship between school refusal behavior and social functioning: a cluster analysis approach. Eur. J. Educ. Psychol. 12, 17–29. doi: 10.30552/ejep.v12il.238
Goos, M., Pipa, J., and Peixoto, F. (2021). Effectiveness of grade retention: a systematic review and meta-analysis. Educ. Res. Rev. 34:100401. doi: 10.1016/j.edurev.2021.100401
Gottfried, M. A. (2014). Chronic absenteeism and its effects on students’ academic and socioemotional outcomes. J. Educ. Stud. Placed Risk 19, 53–75. doi: 10.1080/10824669.2014.962696
Gottfried, M. A. (2019). Chronic absenteeism in the classroom context: effects on achievement. Urban Educ. 54, 3–34. doi: 10.1177/0042085915618709
Gottfried, M. A., and Ansari, A. (2021). Detailing new dangers: linking kindergarten chronic absenteeism to long-term declines in executive functioning. Elem. Sch. J. 121, 484–503. doi: 10.1086/712426
Gottfried, M. A., and Kirksey, J. J. (2017). “When” students miss school: the role of timing of absenteeism on students’ test performance. Educ. Res. 46, 119–130. doi: 10.3102/0013189X17703945
Gottfried, M. A., Stiefel, L., Schwartz, A. E., and Hopkins, B. (2019). Showing up: disparities in chronic absenteeism between students with and without disabilities in traditional public schools. Teach. Coll. Rec. 121, 1–34. doi: 10.1177/01614681191210080
Green, G., DeFosset, A., and Kuo, T. (2019). Residential mobility among elementary school students in Los Angeles County and early school experiences: opportunities for early intervention to prevent absenteeism and academic failure. Front. Psychol. 10:2176. doi: 10.3389/fpsyg.2019.02176
Grooms, A. A., and Bohorquez, D. G. (2022). What’s your excuse? Sensemaking about chronic absenteeism in a rural, Latinx high school. J. Sch. Leadersh. 32, 384–405. doi: 10.1177/10526846211026260
Gubbels, J., van der Put, C. E., and Assink, M. (2019). Risk factors for school absenteeism and dropout: a meta-analytic review. J. Youth Adolesc. 48, 1637–1667. doi: 10.1007/s10964-019-01072-5
Hamlin, D. (2021). Can a positive school climate promote student attendance? Evidence from New York City. Am. Educ. Res. J. 58, 315–342. doi: 10.3102/0002831220924037
Hancock, K. J., Lawrence, D., Shepherd, C. C., Mitrou, F., and Zubrick, S. R. (2017). Associations between school absence and academic achievement: do socioeconomics matter? Br. Educ. Res. J. 43, 415–440. doi: 10.1002/berj.3267
Hansen, C., Sanders, S. L., Massaro, S., and Last, C. G. (1998). Predictors of severity of absenteeism in children with anxiety-based school refusal. J. Clin. Child Psychol. 27, 246–254. doi: 10.1207/s15374424jccp2703_2
Havik, T., Bru, E., and Ertesvåg, S. K. (2014). Parental perspectives of the role of school factors in school refusal. Emot. Behav. Diffic. 19, 131–153. doi: 10.1080/13632752.2013.816199
Havik, T., Bru, E., and Ertesvåg, S. K. (2015). Assessing reasons for school non-attendance. Scand. J. Educ. Res. 59, 316–336. doi: 10.1080/00313831.2014.904424
Havik, T., and Ingul, J. M. (2021). How to understand school refusal. Front. Educ. 6:715177. doi: 10.3389/feduc.2021.715177
Helmich, M. A., Olthof, M., Oldehinkel, A. J., Wichers, M., Bringmann, L. F., and Smit, A. C. (2021). Early warning signals and critical transitions in psychopathology: challenges and recommendations. Curr. Opin. Psychol. 41, 51–58. doi: 10.1016/j.copsyc.2021.02.008
Helmich, M. A., Smit, A. C., Bringmann, L. F., Schreuder, M. J., Oldehinkel, A. J., Wichers, M., et al. (2022). Detecting impending symptom transitions using early-warning signals in individuals receiving treatment for depression. Clin. Psychol. Sci. doi: 10.1177/21677026221137006
Hendron, M., and Kearney, C. A. (2016). School climate and student absenteeism and internalizing and externalizing behavioral problems. Child. Sch. 38, 109–116. doi: 10.1093/cs/cdw009
Heyne, D. A., Sauter, F. M., and Maynard, B. R. (2015). “Moderators and mediators of treatments for youth with school refusal or truancy” in Moderators and mediators of youth treatment outcomes. eds. M. Maric, P. J. M. Prins, and T. H. Ollendick (New York: Oxford), 230–266.
Hobbs, A., Kotlaja, M., and Wylie, L. (2018). Absenteeism interventions: an approach for common definitions in statewide program evaluations. Justice Eval. J. 1, 215–232. doi: 10.1080/24751979.2018.1517584
Hollis, C., Falconer, C. J., Martin, J. L., Whittington, C., Stockton, S., Glazebrook, C., et al. (2017). Annual research review: digital health interventions for children and young people with mental health problems–a systematic and meta-review. J. Child Psychol. Psychiatr. 58, 474–503. doi: 10.1111/jcpp.12663
Howard-Jones, A. R., Bowen, A. C., Danchin, M., Koirala, A., Sharma, K., Yeoh, D. K., et al. (2022). COVID-19 in children: I. epidemiology, prevention, and indirect impacts. J. Paediatr. Child Health 58, 39–45. doi: 10.1111/jpc.15791
Hsu, J., Qin, X., Beavers, S. F., and Mirabelli, M. C. (2016). Asthma-related school absenteeism, morbidity, and modifiable factors. Am. J. Prev. Med. 51, 23–32. doi: 10.1016/j.amepre.2015.12.012
Hughes, E. K., Gullone, E., Dudley, A., and Tonge, B. (2010). A case-control study of emotion regulation and school refusal in children and adolescents. J. Early Adolesc. 30, 691–706. doi: 10.1177/0272431609341049
Hughes, P. M., Ostrout, T. L., and Lewis, S. (2022). The impact of parental and individual factors on school refusal: a multiple-mediation model. J. Fam. Stud. 28, 1488–1503. doi: 10.1080/13229400.2020.1842232
Hughes, J. N., West, S. G., Kim, H., and Bauer, S. S. (2018). Effect of early grade retention on school completion: a prospective study. J. Educ. Psychol. 110, 974–991. doi: 10.1037/edu0000243
Hysing, M., Petrie, K. J., Bøe, T., and Sivertsen, B. (2017). Parental work absenteeism is associated with increased symptom complaints and school absence in adolescent children. BMC Public Health 17:439. doi: 10.1186/s12889-017-4368-7
Ingul, J. M., Havik, T., and Heyne, D. (2019). Emerging school refusal: a school-based framework for identifying early signs and risk factors. Cogn. Behav. Pract. 26, 46–62. doi: 10.1016/j.cbpra.2018.03.005
Iverson, A., French, B. F., Strand, P. S., Gotch, C. M., and McCurley, C. (2018). Understanding school truancy: risk–need latent profiles of adolescents. Assessment 25, 978–987. doi: 10.1177/1073191116672329
Jacobson, M. J., Levin, J. A., and Kapur, M. (2019). Education as a complex system: conceptual and methodological implications. Educ. Res. 48, 112–119. doi: 10.3102/0013189X198269
Jarbou, M., Won, D., Gillis-Mattson, J., and Romanczyk, R. (2022). Deep learning-based school attendance prediction for autistic students. Sci. Rep. 12:1431. doi: 10.1038/s41598-022-05258-z
John, A., Friedmann, Y., DelPozo-Banos, M., Frizzati, A., Ford, T., and Thapar, A. (2022). Association of school absence and exclusion with recorded neurodevelopmental disorders, mental disorders, or self-harm: a nationwide, retrospective, electronic cohort study of children and young people in Wales, UK. Lancet Psychiatry 9, 23–34. doi: 10.1016/S2215-0366(21)00367-9
John-Mora, L., Ross, A. M., and Muroff, J. (2023). “Social disability and impairment in childhood anxiety” in Handbook of child and adolescent anxiety disorders. eds. D. McKay and E. A. Storch (Cham: Springer), 445–467.
Jones, A. M., and Suveg, C. (2015). Flying under the radar: school reluctance in anxious youth. School Ment. Health 7, 212–223. doi: 10.1007/s12310-015-9148-x
Kazdin, A. E. (2019). Annual research review: expanding mental health services through novel models of intervention delivery. J. Child Psychol. Psychiatr. 60, 455–472. doi: 10.1111/jcpp.12937
Kearney, C. A. (2016). Managing school absenteeism at multiple tiers: An evidence-based and practical guide for professionals. New York: Oxford University Press.
Kearney, C. A. (2019). Helping families of youth with school attendance problems: A practical guide for mental health and school-based professionals. New York: Oxford University Press.
Kearney, C. A. (2021). Integrating systemic and analytic approaches to school attendance problems: synergistic frameworks for research and policy directions. Child Youth Care Forum 50, 701–742. doi: 10.1007/s10566-020-09591-0
Kearney, C. A. (2022). Functional impairment guidelines for school attendance problems in youth: recommendations for caseness in the modern era. Prof. Psychol. Res. Pr. 53, 295–303. doi: 10.1037/pro0000453
Kearney, C. A., and Albano, A. M. (2018). When children refuse school: A cognitive-behavioral therapy approach/therapist guide (3rd Edn). New York: Oxford University Press.
Kearney, C. A., and Benoit, L. (2022). Child and adolescent psychiatry and underrepresented youth with school attendance problems: integration with systems of care, advocacy, and future directions. J. Am. Acad. Child Adolesc. Psychiatry 61, 1208–1210. doi: 10.1016/j.jaac.2022.03.016
Kearney, C. A., Benoit, L., Gonzálvez, C., and Keppens, G. (2022). School attendance and school absenteeism: a primer for the past, present, and theory of change for the future. Front Educ. 7:1044608. doi: 10.3389/feduc.2022.1044608
Kearney, C. A., and Childs, J. (2022). Improving school attendance data and defining problematic and chronic school absenteeism: the next stage for educational policies and health-based practices. Prev. Sch. Fail. doi: 10.1080/1045988X.2022.2124222
Kearney, C. A., and Childs, J. (2023). Translating sophisticated data analytic strategies regarding school attendance and absenteeism into targeted educational policy. Improv. Sch. 26, 5–22. doi: 10.1177/13654802231174986
Kearney, C. A., Childs, J., and Burke, S. (2023). Social forces, social justice, and school attendance problems in youth. Contemp. Sch. Psychol. 27, 136–151. doi: 10.1007/s40688-022-00425-5
Kearney, C. A., and Gonzálvez, C. (2022). Unlearning school attendance and its problems: moving from historical categories to postmodern dimensions. Front. Educ. 7:977672. doi: 10.3389/feduc.2022.977672
Kearney, C. A., Gonzálvez, C., Graczyk, P. A., and Fornander, M. (2019a). Reconciling contemporary approaches to school attendance and school absenteeism: toward promotion and nimble response, global policy review and implementation, and future adaptability (part 1). Front. Psychol. 10:2222. doi: 10.3389/fpsyg.2019.02222
Kearney, C. A., Gonzálvez, C., Graczyk, P. A., and Fornander, M. (2019b). Reconciling contemporary approaches to school attendance and school absenteeism: toward promotion and nimble response, global policy review and implementation, and future adaptability (part 2). Front. Psychol. 10:2605. doi: 10.3389/fpsyg.2019.02605
Kearney, C. A., and Graczyk, P. (2014). A response to intervention model to promote school attendance and decrease school absenteeism. Child Youth Care Forum 43, 1–25. doi: 10.1007/s10566-013-9222-1
Kearney, C. A., and Graczyk, P. A. (2020). A multidimensional, multi-tiered system of supports model to promote school attendance and address school absenteeism. Clin. Child. Fam. Psychol. Rev. 23, 316–337. doi: 10.1007/s10567-020-00317-1
Kearney, C. A., and Graczyk, P. A. (2022). Multi-tiered systems of support for school attendance and its problems: an unlearning perspective for areas of high chronic absenteeism. Front Educ. 7:1020150. doi: 10.3389/feduc.2022.1020150
Kiani, C., Otero, K., Taufique, S., and Ivanov, I. (2018). Chronic absenteeism: a brief review of causes, course and treatment. Adolesc. Psychiatry 8, 214–230. doi: 10.2174/2210676608666180709155116
Kim, J., and Gentle-Genitty, C. (2020). Transformative school-community collaboration as a positive school climate to prevent school absenteeism. J. Community Psychol. 48, 2678–2691. doi: 10.1002/jcop.22444
Kim, H., and Page, T. (2013). Emotional bonds with parents, emotion regulation, and school-related behavior problems among elementary school truants. J. Child Fam. Stud. 22, 869–878. doi: 10.1007/s10826-012-9646-5
King, N. J., and Bernstein, G. A. (2001). School refusal in children and adolescents: a review of the past 10 years. J. Am. Acad. Child Adolesc. Psychiatry 40, 197–205. doi: 10.1097/00004583-200102000-00014
Kipp, A. L. (2022). “No one really likes crying in school”: the influences of classroom and institutional dynamics upon student absenteeism during COVID-19. Contin. Educ. 3, 75–91. doi: 10.5334/cie.43
Kipp, A. L., and Clark, J. S. (2022). Student absenteeism and ecological agency. Improv. Sch. 25, 129–147. doi: 10.1177/1365480221992884
Kirshner, B., Van Steenis, E., Pozzoboni, K., and Gaertner, M. (2016). “The costs and benefits of school closure for students” in Learning from the federal market-based reforms. eds. W. J. Mathis and T. M. Trujillo (Charlotte, NC: Information Age Publishing), 201–216.
Klein, M., Sosu, E. M., and Dare, S. (2020). Mapping inequalities in school attendance: the relationship between dimensions of socioeconomic status and forms of school absence. Child Youth Serv. Rev. 118:105432. doi: 10.1016/j.childyouth.2020.105432
Klein, M., Sosu, E. M., and Dare, S. (2022). School absenteeism and academic achievement: does the reason for absence matter? AERA Open 8:23328584211071115. doi: 10.1177/23328584211071115
Knollmann, M., Waltz, E., Reissner, V., Neumann, U., and Hebebrand, J. (2022). Course of school absenteeism 1.5–3 years after initial evaluation: Symptoms, psychosocial functioning, and help-seeking behavior. Z. Kinder Jugendpsychiatr. Psychother. 50, 457–469. doi: 10.1024/1422-4917/a000884
Kohli, R., Pizarro, M., and Nevárez, A. (2017). The “new racism” of K–12 schools: centering critical research on racism. Rev. Res. Educ. 41, 182–202. doi: 10.3102/0091732X16686949
Kruk, M. E., Lewis, T. P., Arsenault, C., Bhutta, Z. A., Irimu, G., Jeong, J., et al. (2022). Improving health and social systems for all children in LMICs: structural innovations to deliver high-quality services. Lancet 399, 7–13. doi: 10.1016/S0140-6736(21)02532-0
Kuhfeld, M., Soland, J., Tarasawa, B., Johnson, A., Ruzek, E., and Liu, J. (2020). Projecting the potential impact of COVID-19 school closures on academic achievement. Educ. Res. 49, 549–565. doi: 10.3102/0013189X20965918
Kumm, S., Wilkinson, S., and McDaniel, S. (2020). Alternative education settings in the United States. Interv. Sch. Clin. 56, 123–126. doi: 10.1177/1053451220914895
Kutsyuruba, B., Klinger, D. A., and Hussain, A. (2015). Relationships among school climate, school safety, and student achievement and well-being: a review of the literature. Rev. Educ. 3, 103–135. doi: 10.1002/rev3.3043
Lall, S. S. (2020). Applying a multicomponent framework to manage school refusal: a case report. Int. J. Behav. Sci. 14, 116–121. doi: 10.30491/ijbs.2020.211632.1174
Langford, R., Bonell, C., Jones, H., Pouliou, T., Murphy, S., Waters, E., et al. (2015). The World Health Organization’s health promoting schools framework: a Cochrane systematic review and meta-analysis. BMC Public Health 15:130. doi: 10.1186/s12889-015-1360-y
Lansford, J. E., Dodge, K. A., Pettit, G. S., and Bates, J. E. (2016). A public health perspective on school dropout and adult outcomes: a prospective study of risk and protective factors from age 5 to 27 years. J. Adolesc. Health 58, 652–658. doi: 10.1016/j.jadohealth.2016.01.014
Lara, J., Noble, K., Pelika, S., and Coons, A. (2018). Chronic absenteeism. NEA research brief. NBI no. 57. Washington, DC: National Education Association.
Lawrence, D., Dawson, V., Houghton, S., Goodsell, B., and Sawyer, M. G. (2019). Impact of mental disorders on attendance at school. Aust. J. Educ. 63, 5–21. doi: 10.1177/0004944118823576
Lee, K., McMorris, B. J., Chi, C. L., Looman, W. S., Burns, M. K., and Delaney, C. W. (2023). Using data-driven analytics and ecological systems theory to identify risk and protective factors for school absenteeism among secondary students. J. Sch. Psychol. 98, 148–180. doi: 10.1016/j.jsp.2023.03.002
Lenhoff, S. W., Edwards, E. B., Claiborne, J., Singer, J., and French, K. R. (2022). A collaborative problem-solving approach to improving district attendance policy. Educ. Policy 36, 1464–1506. doi: 10.1177/0895904820974402
Leroy, Z. C., Wallin, R., and Lee, S. (2017). The role of school health services in addressing the needs of students with chronic health conditions: a systematic review. J. Sch. Nurs. 33, 64–72. doi: 10.1177/1059840516678909
Levine, R. S., Smith, K., and Wagner, N. J. (2022). The impact of callous-unemotional traits on achievement, behaviors, and relationships in school: a systematic review. Child Psychiatry Hum. Dev., 1–21. doi: 10.1007/s10578-022-01344-5
Lewallen, T. C., Hunt, H., Potts-Datema, W., Zaza, S., and Giles, W. (2015). The whole school, whole community, whole child model: a new approach for improving educational attainment and healthy development for students. J. Sch. Health 85, 729–739. doi: 10.1111/josh.12310
Li, A., Guessoum, S. B., Ibrahim, N., Lefèvre, H., Moro, M. R., and Benoit, L. (2021). A systematic review of somatic symptoms in school refusal. Psychosom. Med. 83, 715–723. doi: 10.1097/PSY.0000000000000956
Li, S., and Wang, W. (2022). Effect of blended learning on student performance in K-12 settings: a meta-analysis. J. Comput. Assist. Learn. 38, 1254–1272. doi: 10.1111/jcal.12696
Lindblom, J., Peltola, M. J., Vänskä, M., Hietanen, J. K., Laakso, A., Tiitinen, A., et al. (2017). Early family system types predict children’s emotional attention biases at school age. Int. J. Behav. Dev. 41, 245–256. doi: 10.1177/0165025415620856
Lindholdt, L., Svendsen, K., Rothausen, K. W., and Bech, B. H. (2023). Social well-being and problematic school absence among Danish adolescents: a nationwide cross-sectional study. Scand. J. Public Health. doi: 10.1177/140349482311731
Liverpool, S., Mota, C. P., Sales, C. M., Čuš, A., Carletto, S., Hancheva, C., et al. (2020). Engaging children and young people in digital mental health interventions: systematic review of modes of delivery, facilitators, and barriers. J. Med. Internet Res. 22:e16317. doi: 10.2196/16317
Lystad, R. P., McMaugh, A., Herkes, G., Badgery-Parker, T., Cameron, C. M., and Mitchell, R. J. (2022). The impact of childhood epilepsy on academic performance: a population-based matched cohort study. Seizure 99, 91–98. doi: 10.1016/j.seizure.2022.05.014
Maclean, M. J., Taylor, C. L., and O’Donnell, M. (2020). Adolescent education outcomes and maltreatment: the role of pre-existing adversity, level of child protection involvement, and school attendance. Child Abuse Negl. 109:104721. doi: 10.1016/j.chiabu.2020.104721
Malika, N., Granillo, C., Irani, C., Montgomery, S., and Belliard, J. C. (2021). Chronic absenteeism: risks and protective factors among low-income, minority children and adolescents. J. Sch. Health 91, 1046–1054. doi: 10.1111/josh.13096
Mallett, C. A. (2016). The school-to-prison pipeline: a critical review of the punitive paradigm shift. Child Adolesc. Social Work J. 33, 15–24. doi: 10.1007/s10560-015-0397-1
Marchbanks, M. P., Blake, J. J., Smith, D., Seibert, A. L., and Carmichael, D. (2014). More than a drop in the bucket: the social and economic costs of dropouts and grade retentions associated with exclusionary discipline. J. Appl. Res. Child. 5, 1–36.
Martinot, D., Beaton, A., Tougas, F., Redersdorff, S., and Rinfret, N. (2020). Links between psychological disengagement from school and different forms of self-esteem in the crucial period of early and mid-adolescence. Soc. Psychol. Educ. 23, 1539–1564. doi: 10.1007/s11218-020-09592-w
Martorell, P., and Mariano, L. T. (2018). The causal effects of grade retention on behavioral outcomes. J. Res. Educ. Eff. 11, 192–216. doi: 10.1080/19345747.2017.1390024
Mattison, A., Raffaele Mendez, L. M., Dedrick, R., Dickinson, S., Wingate, E., and Hanks, C. (2018). Early elementary teacher ratings of behavior as predictors of grade retention: race, gender, and socioeconomic status as potential moderators. Psychol. Sch. 55, 1171–1187. doi: 10.1002/pits.22192
Maynard, B. R., Heyne, D., Brendel, K. E., Bulanda, J. J., Thompson, A. M., and Pigott, T. D. (2018). Treatment for school refusal among children and adolescents: a systematic review and meta-analysis. Res. Soc. Work. Pract. 28, 56–67. doi: 10.1177/1049731515598619
Maynard, B. R., Vaughn, M. G., Nelson, E. J., Salas-Wright, C. P., Heyne, D. A., and Kremer, K. P. (2017). Truancy in the United States: examining temporal trends and correlates by race, age, and gender. Child Youth Serv. Rev. 81, 188–196. doi: 10.1016/j.childyouth.2017.08.008
McDermott, E. R., Anderson, S., and Zaff, J. F. (2018). Dropout typologies: relating profiles of risk and support to later educational re-engagement. Appl. Dev. Sci. 22, 217–232. doi: 10.1080/10888691.2016.1270764
Melin, J., Jansson-Fröjmark, M., and Olsson, N. C. (2022). Clinical practitioners’ experiences of psychological treatment for autistic children and adolescents with school attendance problems: a qualitative study. BMC Psychiatry 22:220. doi: 10.1186/s12888-022-03861-y
Melvin, G. A., Freeman, M., Ashford, L. J., Hastings, R. P., Heyne, D., Tonge, B. J., et al. (2023). Types and correlates of school absenteeism among students with intellectual disability. J. Intellect. Disabil. Res. 67, 375–386. doi: 10.1111/jir.13011
Melvin, G. A., Heyne, D., Gray, K. M., Hastings, R. P., Totsika, V., Tonge, B. J., et al. (2019). The kids and teens at school (KiTeS) framework: an inclusive bioecological systems approach to understanding school absenteeism and school attendance problems. Front. Educ. 4:61. doi: 10.3389/feduc.2019.00061
Mital, P., Moore, R., and Llewellyn, D. (2014). Analyzing K-12 education as a complex system. Procedia Comput. Sci. 28, 370–379. doi: 10.1016/j.procs.2014.03.046
Mitchell, B. S., Kern, L., and Conroy, M. A. (2019). Supporting students with emotional or behavioral disorders: state of the field. Behav. Disord. 44, 70–84. doi: 10.1177/0198742918816518
National Forum on Education Statistics. (2021). Forum guide to attendance, participation, and engagement data in virtual and hybrid learning models (NFES2021058). Washington, DC: U.S. Department of Education.
Nayak, A., Sangoi, B., and Nachane, H. (2018). School refusal behavior in Indian children: analysis of clinical profile, psychopathology and development of a best-fit risk assessment model. Indian J. Pediatr. 85, 1073–1078. doi: 10.1007/s12098-018-2631-2
Nelson, B., McGorry, P. D., Wichers, M., Wigman, J. T., and Hartmann, J. A. (2017). Moving from static to dynamic models of the onset of mental disorder: a review. JAMA Psychiat. 74, 528–534. doi: 10.1001/jamapsychiatry.2017.0001
Newman, I., Ligas, M. R., Hecht, S., Starratt, G. K., Clement, R., Ney, E., et al. (2019). Mixed methods assessment of the dimensionality of risk indicators of school failure: a collaborative approach to bridge a research-to-practice gap. Int. J. Mult. Res. Approaches 11, 156–182. doi: 10.29034/ijmra.v11n2a3
Nichols, L. M., Mueller, S., and Donisthorpe, K. (2021). School refusal in a multi-tiered system of supports model: cognitive-behavioral and mindfulness interventions. J. Sch. Couns. 19, 1–30.
Niemi, S., Lagerström, M., and Alanko, K. (2022). School attendance problems in adolescent with attention deficit hyperactivity disorder. Front. Psychol. 13:7367. doi: 10.3389/fpsyg.2022.1017619
Oliver, T. H., Heard, M. S., Isaac, N. J., Roy, D. B., Procter, D., Eigenbrod, F., et al. (2015). Biodiversity and resilience of ecosystem functions. Trends Ecol. Evol. 30, 673–684. doi: 10.1016/j.tree.2015.08.009
Pangrazio, L., Selwyn, N., and Cumbo, B. (2023). A patchwork of platforms: mapping data infrastructures in schools. Learn. Media Technol. 48, 65–80. doi: 10.1080/17439884.2022.2035395
Patrick, S., and Chambers, A. (2020). Determining attendance and alternatives to seat-time: Issue brief. Vienna, VA: Aurora Institute.
Peguero, A. A., Marchbanks, M. P. T. III, Varela, K. S., Eason, J. M., and Blake, J. (2018). Too strict or too lenient?: examining the role of school strictness with educational and juvenile justice outcomes. Sociol. Spectr. 38, 223–242. doi: 10.1080/02732173.2018.1478350
Pengpid, S., and Peltzer, K. (2019). Bullying victimization and externalizing and internalizing symptoms among in-school adolescents from five ASEAN countries. Child Youth Serv. Rev. 106:104473. doi: 10.1016/j.childyouth.2019.104473
Persich, M. R., and Robinson, M. D. (2022). Five approaches to understanding interpersonal competence: a review and integration. Rev. Gen. Psychol. 26, 464–486. doi: 10.1177/10892680221085507
Pikulski, P. J., Pella, J. E., Casline, E. P., Hale, A. E., Drake, K., and Ginsburg, G. S. (2020). School connectedness and child anxiety. J. Psychol. Couns. Sch. 30, 13–24. doi: 10.1017/jgc.2020.3
Piscitello, J., Kim, Y. K., Orooji, M., and Robison, S. (2022). Sociodemographic risk, school engagement, and community characteristics: a mediated approach to understanding high school dropout. Child Youth Serv. Rev. 133:106347. doi: 10.1016/j.childyouth.2021.106347
Polanin, J. R., Espelage, D. L., Grotpeter, J. K., Spinney, E., Ingram, K. M., Valido, A., et al. (2021). A meta-analysis of longitudinal partial correlations between school violence and mental health, school performance, and criminal or delinquent acts. Psychol. Bull. 147, 115–133. doi: 10.1037/bul0000314
Pyne, J., Grodsky, E., Vaade, E., McCready, B., Camburn, E., and Bradley, D. (2023). The signaling power of unexcused absence from school. Educ. Policy 37, 676–704. doi: 10.1177/08959048211049428
Radez, J., Reardon, T., Creswell, C., Lawrence, P. J., Evdoka-Burton, G., and Waite, P. (2021). Why do children and adolescents (not) seek and access professional help for their mental health problems? A systematic review of quantitative and qualitative studies. Eur. Child Adolesc. Psychiatry 30, 183–211. doi: 10.1007/s00787-019-01469-4
Rahman, M. A., Renzaho, A. M., Kundu, S., Awal, M. A., Ashikuzzaman, M., Fan, L., et al. (2023). Prevalence and factors associated with chronic school absenteeism among 207,107 in-school adolescents: findings from cross-sectional studies in 71 low-middle and high-income countries. PLoS One 18:e0283046. doi: 10.1371/journal.pone.0283046
Raviola, G., Naslund, J. A., Smith, S. L., and Patel, V. (2019). Innovative models in mental health delivery systems: task sharing care with non-specialist providers to close the mental health treatment gap. Curr. Psychiatry Rep. 21, 1–13. doi: 10.1007/s11920-019-1028-x
Redmond, S. M., and Hosp, J. L. (2008). Absenteeism rates in students receiving services for CDs, LDs, and EDs: a macroscopic view of the consequences of disability. Lang. Speech Hear. Serv. Sch. 39, 97–103. doi: 10.1044/0161-1461(2008/010)
Reimer, T., and Hill, J. C. (2022). Crossing the digital divide and the equity expanse: reaching and teaching all students during the pandemic. J. Leadersh. Equity Res. 8, 71–86.
Reyes, A. (2020). Compulsory school attendance: the new American crime. Educ. Sci. 10:75. doi: 10.3390/educsci10030075
Rhodes, J., Thomas, J. M., and Liles, A. R. (2018). Predictors of grade retention among children in an elementary school truancy intervention. J. Risk Issues 21, 1–10.
Richardson, E. W., Grogan, C. S., Richardson, S. L., and Small, S. L. (2018). Displacement, caregiving, and the ecological system of youth in foster care: a theoretical perspective. J. Fam. Soc. Work. 21, 348–364. doi: 10.1080/10522158.2018.1469561
Rocque, M., Jennings, W. G., Piquero, A. R., Ozkan, T., and Farrington, D. P. (2017). The importance of school attendance: findings from the Cambridge study in delinquent development on the life-course effects of truancy. Crime Delinq. 63, 592–612. doi: 10.1177/0011128716660520
Ruff, R. R., Senthi, S., Susser, S. R., and Tsutsui, A. (2019). Oral health, academic performance, and school absenteeism in children and adolescents: a systematic review and meta-analysis. J. Am. Dent. Assoc. 150, 111–121. doi: 10.1016/j.adaj.2018.09.023
Rumberger, R. W. (2020). “The economics of high school dropouts” in The economics of education: A comprehensive overview. eds. S. Bradley and C. Green. 2nd ed (Cambridge, MA: Academic), 149–158.
Rutter, H., Savona, N., Glonti, K., Bibby, J., Cummins, S., Finegood, D. T., et al. (2017). The need for a complex systems model of evidence for public health. Lancet 390, 2602–2604. doi: 10.1016/S0140-6736(17)31267-9
Santibañez, L., and Guarino, C. M. (2021). The effects of absenteeism on academic and social-emotional outcomes: lessons for COVID-19. Educ. Res. 50, 392–400. doi: 10.3102/0013189X21994488
Scharfstein, L. A., and Beidel, D. C. (2015). Social skills and social acceptance in children with anxiety disorders. J. Clin. Child Adolesc. Psychol. 44, 826–838. doi: 10.1080/15374416.2014.895938
Scheffer, M., Bascompte, J., Brock, W. A., Brovkin, V., Carpenter, S. R., Dakos, V., et al. (2009). Early-warning signals for critical transitions. Nat 461, 53–59. doi: 10.1038/nature08227
Schellpfeffer, N., Collins, A., Brousseau, D. C., Martin, E. T., and Hashikawa, A. (2017). Web-based surveillance of illness in childcare centers. Health Secur. 15, 463–472. doi: 10.1089/hs.2016.0124
Schoon, I. (2021). Towards an integrative taxonomy of social-emotional competences. Front. Psychol. 12:515313. doi: 10.3389/fpsyg.2021.515313
Schreuder, M. J., Hartman, C. A., George, S. V., Menne-Lothmann, C., Decoster, J., van Winkel, R., et al. (2020). Early warning signals in psychopathology: what do they tell? BMC Med. 18, 1–11. doi: 10.1186/s12916-020-01742-3
Sheridan, S. M., Smith, T. E., Moorman Kim, E., Beretvas, S. N., and Park, S. (2019). A meta-analysis of family-school interventions and children’s social-emotional functioning: moderators and components of efficacy. Rev. Educ. Res. 89, 296–332. doi: 10.3102/0034654318825437
Singer, J., Pogodzinski, B., Winchell Lenhoff, D., and Cook, W. (2021). Advancing an ecological approach to chronic absenteeism: evidence from Detroit. Teach. Coll. Rec. 123:040306. doi: 10.1177/016146812112300406
Skedgell, K. K., and Kearney, C. A. (2018). Predictors of school absenteeism severity at multiple levels: a classification and regression tree analysis. Child Youth Serv. Rev. 86, 236–245. doi: 10.1016/j.childyouth.2018.01.043
Smerillo, N. E., Reynolds, A. J., Temple, J. A., and Ou, S. R. (2018). Chronic absence, eighth-grade achievement, and high school attainment in the Chicago longitudinal study. J. Sch. Psychol. 67, 163–178. doi: 10.1016/j.jsp.2017.11.001
Smythe-Leistico, K., and Page, L. C. (2018). Connect-text: leveraging text-message communication to mitigate chronic absenteeism and improve parental engagement in the earliest years of schooling. J. Educ. Stud. Placed Risk 23, 139–152. doi: 10.1080/10824669.2018.1434658
Sokol, R., Austin, A., Chandler, C., Byrum, E., Bousquette, J., Lancaster, C., et al. (2019). Screening children for social determinants of health: a systematic review. Pediatr. 144:e20191622. doi: 10.1542/peds.2019-1622
Sosu, E. M., Dare, S., Goodfellow, C., and Klein, M. (2021). Socioeconomic status and school absenteeism: a systematic review and narrative synthesis. Rev. Educ. 9:e3291. doi: 10.1002/rev3.3291
Stein, M. L., and Grigg, J. A. (2019). Missing bus, missing school: establishing the relationship between public transit use and student absenteeism. Am. Educ. Res. J. 56, 1834–1860. doi: 10.3102/000283121983391
Stempel, H., Cox-Martin, M., Bronsert, M., Dickinson, L. M., and Allison, M. A. (2017). Chronic school absenteeism and the role of adverse childhood experiences. Acad. Pediatr. 17, 837–843. doi: 10.1016/j.acap.2017.09.013
Stoiber, K. C., and Gettinger, M. (2016). “Multi-tiered systems of support and evidence-based practices,” in Handbook of response to intervention: The science and practice of multi-tiered systems of support. 2nd Edneds. M. K. Jimerson, A. M. Burns, and A. M. DerHeydenVan, New York, NY: Springer, 124–141.
Sugrue, E. P., Zuel, T., and LaLiberte, T. (2016). The ecological context of chronic school absenteeism in the elementary grades. Child. Sch. 38, 137–145. doi: 10.1093/cs/cdw020
Tambling, R. R., D’Aniello, C., and Russell, B. S. (2021). Mental health literacy: a critical target for narrowing racial disparities in behavioral health. Int. J. Ment. Health Addict., 1–15. doi: 10.1007/s11469-021-00694-w
Thapa, A., and Cohen, J. (2017). School climate community scale: report on construct validity and internal consistency. Sch. Comm. J. 7, 303–320.
Thingholm, P. R., Gaulke, A., Eriksen, T. M., Svensson, J., and Skipper, N. (2020). Association of prodromal type 1 diabetes with school absenteeism of Danish schoolchildren: a population-based case-control study of 1,338 newly diagnosed children. Diabetes Care 43, 2886–2888. doi: 10.2337/dc20-0769
Thomas, K. J., de Cunha, J. M., de Souza, D. A., and Santo, J. (2019). Fairness, trust, and school climate as foundational to growth mindset: a study among Brazilian children and adolescents. Educ. Psychol. 39, 510–529. doi: 10.1080/01443410.2018.1549726
Todić, J., Cubbin, C., Armour, M., Rountree, M., and González, T. (2020). Reframing school-based restorative justice as a structural population health intervention. Health Place 62:102289. doi: 10.1016/j.healthplace.2020.102289
Totsika, V., Hastings, R. P., Dutton, Y., Worsley, A., Melvin, G., Gray, K., et al. (2020). Types and correlates of school non-attendance in students with autism spectrum disorders. Autism 24, 1639–1649. doi: 10.1111/jir.13011
Tsang, T. K., Huang, X., Guo, Y., Lau, E. H., Cowling, B. J., and Ip, D. K. (2023). Monitoring school absenteeism for influenza-like illness surveillance: systematic review and meta-analysis. JMIR Public Health Surveill. 9:e41329. doi: 10.2196/41329
Tucker, M. C., and Rodriguez, C. M. (2014). Family dysfunction and social isolation as moderators between stress and child physical abuse risk. J. Fam. Violence 29, 175–186. doi: 10.1007/s10896-013-9567-0
UNESCO. (2019). Combining data on out-of-school children, completion and learning to offer a more comprehensive view on SDG 4. Montreal: UNESCO Institute for Statistics.
United Nations General Assembly (1948). The universal declaration of human rights (UDHR). New York: United Nations General Assembly.
Valbuena, J., Mediavilla, M., Choi, Á., and Gil, M. (2021). Effects of grade retention policies: a literature review of empirical studies applying causal inference. J. Econ. Surv. 35, 408–451. doi: 10.1111/joes.12406
Van Ameringen, M., Mancini, C., and Farvolden, P. (2003). The impact of anxiety disorders on educational achievement. J. Anxiety Disord. 17, 561–571. doi: 10.1016/S0887-6185(02)00228-1
Van Eck, K., Johnson, S. R., Bettencourt, A., and Johnson, S. L. (2017). How school climate relates to chronic absence: a multi–level latent profile analysis. J. Sch. Psychol. 61, 89–102. doi: 10.1016/j.jsp.2016.10.001
Wainberg, M. L., Scorza, P., Shultz, J. M., Helpman, L., Mootz, J. J., Johnson, K. A., et al. (2017). Challenges and opportunities in global mental health: a research-to-practice perspective. Curr. Psychiatry Rep. 19, 1–10. doi: 10.1007/s11920-017-0780-z
Wang, M. T., Degol, J. L., Amemiya, J., Parr, A., and Guo, J. (2020). Classroom climate and children’s academic and psychological wellbeing: a systematic review and meta-analysis. Dev. Rev. 57:100912. doi: 10.1016/j.dr.2020.100912
Wang, M. T., Scanlon, C. L., and Del Toro, J. (2023). Does anyone benefit from exclusionary discipline? An exploration on the direct and vicarious links between suspensions for minor infraction and adolescents’ academic achievement. Am. Psychol. 78, 20–35. doi: 10.1037/amp0001030
Wanzer, D., Postlewaite, E., and Zargarpour, N. (2019). Relationships among noncognitive factors and academic performance: testing the University of Chicago Consortium on school research model. AERA Open 5:2332858419897275. doi: 10.1177/2332858419897275
Warne, M., Svensson, Å., Tirén, L., and Wall, E. (2020). On time: a qualitative study of Swedish students’, parents’ and teachers’ views on school attendance, with a focus on tardiness. Int. J. Environ. Res. Public Health 17:1430. doi: 10.3390/ijerph17041430
Welsh, R. O. (2018). Opposite sides of the same coin? Exploring the connections between school absenteeism and student mobility. J. Educ. Stud. Placed Risk 23, 70–92. doi: 10.1080/10824669.2018.1438204
West, M. R., Buckley, K., Krachman, S. B., and Bookman, N. (2018). Development and implementation of student social-emotional surveys in the CORE districts. J. Appl. Dev. Psychol. 55, 119–129. doi: 10.1016/j.appdev.2017.06.001
Wichers, M., Schreuder, M. J., Goekoop, R., and Groen, R. N. (2019). Can we predict the direction of sudden shifts in symptoms? Transdiagnostic implications from a complex systems perspective on psychopathology. Psychol. Med. 49, 380–387. doi: 10.1017/S0033291718002064
Wilkins, J., and Bost, L. W. (2016). Dropout prevention in middle and high schools: from research to practice. Interv. Sch. Clin. 51, 267–275. doi: 10.1177/1053451215606697
Williams, T. T., and Sánchez, B. (2011). Identifying and decreasing barriers to parent involvement for inner-city parents. Youth Soc. 45, 54–74. doi: 10.1177/0044118X11409066
Wright, A. G., and Woods, W. C. (2020). Personalized models of psychopathology. Annu. Rev. Clin. Psychol. 16, 49–74. doi: 10.1146/annurev-clinpsy-102419-125032
Yoon, S., Quinn, C. R., Shockley McCarthy, K., and Robertson, A. A. (2021). The effects of child protective services and juvenile justice system involvement on academic outcomes: gender and racial differences. Youth Soc. 53, 131–152. doi: 10.1177/0044118X19844392
Zaff, J. F., Donlan, A., Gunning, A., Anderson, S. E., McDermott, E., and Sedaca, M. (2017). Factors that promote high school graduation: a review of the literature. Educ. Psychol. Rev. 29, 447–476. doi: 10.1007/s10648-016-9363-5
Keywords: school attendance, school attendance problems, school absenteeism, complex systems theory, early warning signals, functional impairment
Citation: Kearney CA, Dupont R, Fensken M and Gonzálvez C (2023) School attendance problems and absenteeism as early warning signals: review and implications for health-based protocols and school-based practices. Front. Educ. 8:1253595. doi: 10.3389/feduc.2023.1253595
Edited by:
Michael R. Hass, Chapman University, United StatesReviewed by:
Martin Karlberg, Uppsala University, SwedenZack Maupin, Chapman University, United States
Copyright © 2023 Kearney, Dupont, Fensken and Gonzálvez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Christopher A. Kearney, Y2hyaXMua2Vhcm5leUB1bmx2LmVkdQ==