Learning difficulties are known to increase the risk of unemployment and lower levels of education (Hakkarainen et al., 2015; McLaughlin et al., 2014). The prevalence of learning difficulties is estimated to be between 3 – 15% among school-aged children (Arrhenius, 2022). As reading difficulties being the most investigated subtype of learning difficulties, it is known that reading difficulties are associated with risk of employment in low status occupation (Smart et al., 2017) and poorer academic achievement (Hakkarainen et al., 2013a). On this matter, mathematical difficulties have gained less attention. However, in recent years there has been increasing interest in mathematical difficulties, and they have been shown to pose even greater risk for educational attainment and lower levels of education, compared to difficulties in reading (Hakkarainen et al., 2013; Hakkarainen et al., 2015; Psyridou et al., 2024). 

Besides learning difficulties, several students have Attention Deficit/Hyperactivity Disorder (ADHD) (Wei et al., 2014). Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by a longstanding and frequent occurrence of inattention and /or hyperactivity-impulsivity that interferes with functioning or development (THL, 2014). ADHD is very heterogenous in nature, and it is associated with different behavioral and cognitive deficits (Aro et al., 2024; Hollingdale et al., 2023; Loyer Carbonneau et al., 2021).  Students with ADHD show more often poorer academic outcomes, grade repetition and increased risk of dropping out of education compared to children without ADHD (Fried et al., 2016; Palmu et al., 2018; Zendarski et al., 2022). 

While the individual effects of reading difficulties, mathematical difficulties, and ADHD on educational outcomes have been studied, there is insufficient understanding of which types of difficulties influences most strongly a student’s academic trajectory and, ultimately, their access to further education or employment. The aim of this study is to examine the extent to which learning difficulties in reading comprehension, difficulties in technical reading and difficulties in arithmetic skills and ADHD predict non-graduation of upper secondary education on targeted time, and whether these studied factors increase the risk for ending up in social assistance in young adulthood. In this study, we examined separately the effects of ADHD diagnosis and self-reported ADHD symptoms measured in 9^th grade.

Furthermore, in some cases a young person requires additional support to manage their daily life and studies due to certain illness or impairment (e.g. learning/developmental disabilities, mental health issues or conduct problems). In Finland, there are rehabilitation allowances, that are available for young people whose capacity for work and study has decreased. The purpose of the rehabilitation allowances is to support participation education, enhance their employment prospects and ensure financial security. These allowances are provided by Kela. Kela is the Social Insurance Institution of Finland, which is a government agency that provides basic economic security for everyone living in Finland. The aim of this study is to identify possible associations between different types of learning difficulties, ADHD and the receipt of rehabilitation allowances.

The research questions are:

1.To what extent do learning difficulties (technical reading skills, reading comprehension and arithmetic skills) in Grade 9 and ADHD (ADHD diagnosis and ADHD self-reported symptoms measured in Grade 9) predict not graduating from upper secondary education on time and ending up on basic social assistance in young adulthood?

a)Does school achievement mediate the effect of learning difficulties and ADHD on educational attainment and ending up on basic social assistance?

2.To what extent do learning difficulties and ADHD predict the number of months of rehabilitation allowance received?

This study included a sample that was drawn from a follow-up study, which was collected in two parts. In the first part of the follow-up study, approximately 2000 students were followed from early childhood education to the end of lower secondary school between 2006 and 2016. In its extension, the participants were followed during upper secondary education until the year 2019. In Finland, upper secondary education lasts typically three years. When students were in the ninth grade in 2016, their mathematical and reading skills were tested, and participants filled in The Strengths and Difficulties Questionnaire (SDQ), which was used to measure self-reported ADHD symptoms. “Having difficulties” was defined as belonging to the lowest-performing group of the test scores. In 2019, information about graduation from upper secondary education was collected from school registers. Additionally, this study uses the register data from Kela (the Social Insurance Institution of Finland). Specifically, information on the number of months a participant received basic social assistance in 2021 was gathered from Kela's registers. For this study, three categories were created to indicate the number of months the applicant received basic social assistance within a year (1 = 0 months, 2 = 1-4 months, 3 = 5-12 months). Information on whether a participant received medical reimbursement for ADHD during the years 2005-2021 was collected from Kela registers. The used ATC – codes (The Anatomical Therapeutic Chemical – code) for ADHD were methylphenidate (ATC – class N06BA04), atomoxetine (N06BA09), lisdexamfetamine (N06BA12) and dexamphetamine (N06BA02). There were 62 participants who had received medical reimbursement for the medication mentioned above. Furthermore, there were 5 participants in the sample that had received other forms of support from Kela. In total 67 participants had an ADHD diagnosis. Also, information on four different rehabilitation allowances were available: Rehabilitation Allowance for Young People, Vocational Rehabilitation for Young People, and a rehabilitation allowance provided on the basis of Act on Rehabilitation Provided by Kela. The information on different rehabilitation allowances was combined on the basis of how many months a participant received rehabilitation allowance ( n = 106 ). The variable was continuous, indicating the total amount of months allowance received. To address the research questions, data were analysed using structural equation modeling (SEM). Since one outcome variable was dichotomous and another one categorized in 3 categories, logistic regression was used. Analysis was performed using the Mplus software (Version 8.8).

The preliminary results indicate that when the main predictors were in the model simultaneously self-reported ADHD symptoms examined in the ninth grade and ADHD diagnosis predicted noncompletion of upper secondary education on targeted time. Furthermore, if the student had an ADHD diagnosis or they reported ADHD symptoms in 9th grade, the odds of receiving basic social assistance in year 2021 increased significantly. Also, difficulties in arithmetic skills increased the odds of receiving basic social assistance. From the predictive main variables, only ADHD diagnosis predicted increased number of months rehabilitation allowance received. Also familial socioeconomic background and students´ educational track in upper secondary education was statistically significantly associated with increased number of months rehabilitation allowance received. When the mediating effects of academic achievement in literacy and mathematics were examined, the preliminary results indicated that academic achievement seems to mediate the relationship between difficulties in arithmetic skills and ending up in social assistance. Other previous direct effects remained the same. These result indicate, that diagnosed ADHD poses a very severe risk for noncompletion of upper secondary education on targeted time, as well as increasing the dependency on social benefits, such as basic social assistance and rehabilitation allowance. Also, many students may have ADHD without a diagnosis and the results indicate that self-reported symptoms of ADHD also increase the risk of noncompletion of upper secondary education on time. Furthermore, of learning difficulties -variables, only difficulties in arithmetic skills predicted ending up on social assistance in young adulthood.

Aro, T., Eklund, K., Hynd, G., & Ahonen, T. (2024). Associations of inattention, hyperactivity, and sex with behavioral–emotional symptoms among children with mathematical disability. Children and youth services review, 162, 107717. https://doi.org/10.1016/j.childyouth.2024.107717 Fredriksen, M., Dahl, A. A., Martinsen, E. W., Klungsoyr, O., Faraone, S. V., & Peleikis, D. E. (2014). Childhood and persistent ADHD symptoms associated with educational failure and long-term occupational disability in adult ADHD. Attention deficit and hyperactivity disorders, 6(2), 87-99. https://doi.org/10.1007/s12402-014-0126-1 Fried, R., Petty, C., Faraone, S. V., Hyder, L. L., Day, H., & Biederman, J. (2016). Is ADHD a Risk Factor for High School Dropout? A Controlled Study. Journal of attention disorders, 20(5), 383-389. https://doi.org/10.1177/1087054712473180 Hakkarainen, A. M., Holopainen, L. K., & Savolainen, H. K. (2015). A five-year follow-up on the role of educational support in preventing dropout from upper secondary education in Finland. Journal of Learning Disabilities, 48(4), 408–421. https://doi.org/10.1177/0022219413507603 Langer, N., Benjamin, C., Becker, B. L. C., & Gaab, N. (2019). Comorbidity of reading disabilities and ADHD: Structural and functional brain characteristics. Human brain mapping, 40(9), 2677-2698. https://doi.org/10.1002/hbm.24552 McLaughlin, M. J., Speirs, K. E., & Shenassa, E. D. (2014). Reading Disability and Adult Attained Education and Income: Evidence From a 30-Year Longitudinal Study of a Population-Based Sample. Journal of learning disabilities, 47(4), 374-386. https://doi.org/10.1177/0022219412458323 Psyridou, M., Prezja, F., Torppa, M., Lerkkanen, M., Poikkeus, A., & Vasalampi, K. (2024). Machine learning predicts upper secondary education dropout as early as the end of primary school. Scientific reports, 14(1), 12956. https://doi.org/10.1038/s41598-024-63629-0 Smart, D., Youssef, G. J., Sanson, A., Prior, M., Toumbourou, J. W., & Olsson, C. A. (2017). Consequences of childhood reading difficulties and behaviour problems for educational achievement and employment in early adulthood. British journal of educational psychology, 87(2), 288-308. https://doi.org/10.1111/bjep.12150 Wei, X., Yu, J. W., & Shaver, D. (2014). Longitudinal Effects of ADHD in Children with Learning Disabilities or Emotional Disturbances. Exceptional children, 80(2), 205-219. https://doi.org/10.1177/001440291408000205