Reflections on the Field of School Attendance Problems: For the Times They Are a-Changing?

School Refusal in Youth: A Systematic Review of Ecological Factors

To guide school practitioners in the identification and intervention of youth with anxious school refusal, this systematic review used an ecological lens to examine the factors that differentiated children and adolescents with school refusal from those without. Based on the rigorous protocol from the Center for Reviews and Dissemination's (CRD) internationally recognized guidelines, 15 studies examining 67 different factors were identified. Results reveal 44 individual, social and contextual factors that differentiate youth with school refusal from peers without school refusal. Findings highlight the centrality of anxiety, or anxiety-related symptoms, and diverse learning needs as main points of contrast between youth with school refusal and those without. Implications of an ecological understanding of the factors associated with school refusal for selective and indicative prevention by school and mental health practitioners are discussed.

Deep learning-based school attendance prediction for autistic students

Autism Spectrum Disorder is a neurodevelopmental disorder characterized by deficits in social communication and interaction as well as the presence of repetitive, restricted patterns of behavior, interests, or activities. Many autistic students experience difficulty with daily functioning at school and home. Given these difficulties, regular school attendance is a primary source for autistic students to receive an appropriate range of needed educational and therapeutic interventions. Moreover, school absenteeism (SA) is associated with negative consequences such as school drop-out. Therefore, early SA prediction would help school districts to intervene properly to ameliorate this issue. Due to its heterogeneity, autistic students show within-group differences concerning their SA. A comprehensive statistical analysis performed by the authors shows that the individual and demographic characteristics of the targeted population are not predictive factors of SA. So, we used the students’ recent previous attendance to predict their future attendance. We introduce a deep learning-based framework for predicting short-and long-term SA of autistic students using the Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) algorithms. The adopted algorithms outperform other machine learning algorithms. In detail, LSTM increased the accuracy and recall of short-term SA prediction by 20% and 13%, while the same scores of long-term SA prediction increased by 5% using MLP.

A Scoping Review of Constructs Measured Following Intervention for School Refusal: Are We Measuring Up?

Reviews of the effectiveness of interventions for school refusal (SR) rely upon well-conducted primary studies. Currently there are no guidelines for those conducting primary studies about the measurement of outcome following intervention for SR. Most people would agree that it is important to measure school attendance as an outcome but there has been little discussion about other constructs that warrant measurement. To facilitate this discussion and support the development of guidelines, we conducted a scoping review of constructs measured in studies evaluating intervention for SR. We screened the title and abstract of 3,213 publications found in peer-reviewed journals between 1980 and 2019. After full text review of 271 publications, 50 publications describing 51 studies were included. Results address the frequency with which constructs were measured, along with instruments used, informants, and time-points for measurement. Based on the results, we offer guidelines for choosing constructs to measure following intervention for SR and considerations for how to measure the constructs. Guidelines can increase consistency across primary studies, with benefits for future meta-analyses and international comparisons. They also provide support for practitioners contemplating routine evaluation of their interventions for SR. Ultimately, a core outcome set for SR can be developed.

The Association Between Symptoms of Depression and School Absence in a Population-Based Study of Late Adolescents

OBJECTIVE

School attendance is an important functional marker in adolescence, and knowledge of the correlates of school absence is important to inform preventive efforts. The main aim of the present study was to investigate the association between symptoms of depression and school absence in late adolescence, adjusting for sociodemographic characteristics and externalizing problems.

METHODS

Data stem from the youth@hordaland-survey, a population-based survey of adolescents between 16 and 19 years old attending upper secondary education in Hordaland County, Norway, in spring 2012. Administrative data on school absence was provided for 8222 adolescents. In addition to days and hours absent the past semester, a variable of total absence was calculated and divided into quartiles of absence. Symptoms of mental health problems and sleep duration was based on adolescent self-reports.

RESULTS

Reports of depressive symptoms were significantly associated with school absence when investigated as continuous variables. The strength of the association attenuated but remained statistically significant when controlling for sociodemographic factors and externalizing problems. When investigating the association at different levels of school absence, adolescents in the second, third and fourth quartile of school absence reported significantly higher depression scores compared to adolescents in the first quartile. The association between reports of symptoms of depression and school absence was partially mediated by sleep duration.

CONCLUSION

The association between reported symptoms of depression and school absence was evident even at low levels of school absence, indicating a role for universal prevention strategies. The findings suggest both depression and sleep problems as possible targets for intervention in late adolescence.

Types and correlates of school non-attendance in students with autism spectrum disorders

School non-attendance in autism spectrum disorders has received very little attention to date. The study aimed to provide a comprehensive description of school non-attendance in students with autism spectrum disorders. Through an online survey, parents of 486 children (mean age: 11 years) reported on school attendance over 1 month and reasons for instances of non-attendance. On average, students missed 5 days of school of a possible 23 days. Persistent non-attendance (absent on 10%+ of available sessions) occurred among 43% of students. School non-attendance was associated with child older age, not living in a two-parent household, parental unemployment and, especially, attending a mainstream school. School refusal accounted for 43% of non-attendance. School exclusion and school withdrawal each accounted for 9% of absences. Truancy was almost non-existent. Non-problematic absenteeism (mostly related to medical appointments and illness) accounted for 32% of absences. Non-problematic absenteeism was more likely among those with intellectual disability, school refusal was more likely among older students and school exclusion was more likely among students from single-parent, unemployed and well-educated households. Findings suggest that school non-attendance in autism spectrum disorders is a significant issue, and that it is important to capture detail about attendance patterns and reasons for school non-attendance.

Measuring School Absenteeism: Administrative Attendance Data Collected by Schools Differ From Self-Reports in Systematic Ways

In order to use attendance monitoring within an integrative strategy for preventing, assessing and addressing cases of youth with school absenteeism, we need to know whether the attendance data collected by schools cover all students with (emerging) school attendance problems (SAPs). The current article addresses this issue by comparing administrative attendance data collected by schools with self-reported attendance data from the same group of students (age 15–16) in Flanders, the Dutch-speaking part of Belgium (N = 4344). We seek to answer the following question: does an estimation of unauthorized absenteeism based on attendance data as collected by schools through electronic registration differ from self-reported unauthorized absenteeism and, if so, are the differences between administrative and self-reported unauthorized absenteeism systematic? Our results revealed a weak association between self-reported unauthorized school absenteeism and registered unauthorized school absenteeism. Boys, students in technical and vocational tracks and students who speak a foreign language at home, with a less-educated mother and who receive a school allowance, received more registered unauthorized absences than they reported themselves. In addition, pupils with school refusal and who were often authorized absent from school received more registered unauthorized absences compared to their self-reported unauthorized school absenteeism. In the discussion, we elaborate on the implications of our findings.

Working with school-refusing young people in Tower Hamlets, London

This study looks at the local context, describing quantitative data from a subset of young people seen within the inner London Tower Hamlets Pupil Referral Unit (PRU) that are extremely socially withdrawn and unable to attend mainstream education. The core aim of this research was to examine the views of professionals who work with and for these young people. Qualitative group interviews were used to gain a deeper understanding of the needs of these young people, what might contribute to their withdrawal from school and what needs to be done to help them reintegrate. Professionals reported that these young people were highly complex in terms of their needs and presentation and that there is a lack of clarity around what causes these young people to withdraw. They agreed that a more intensive multilayered intervention was required to meet their needs. Interventions that include gradual socialization, parental involvement and which address the role of technology were indicated. However, more research is needed to clarify how to effectively intervene.