School Refusal Behavior Profiles, Optimism/Pessimism, and Personality Traits in Spanish Children

School refusal profiles maintained by negative reinforcement and their relationship with self-perceived health

Health alterations and school refusal behavior may significantly affect student evolution in all areas of student lives. The objective of this study was to use latent profile analysis to identify school refusal profiles sustained by negative reinforcement and to determine their relationship with distinct self-perceived health variables (Satisfaction, Well-being, Resilience, Performance, and Risk-Taking). The School Refusal Assessment Scale-Revised (SRAS-R) and the Child Health and Illness Profile (CHIP-CE/CRF) were administered to 737 students (60.9% male) aged between 8 and 10 (M = 8.76, SD = 0.74). Three profiles of school refusal maintained by negative reinforcement were obtained: no risk, moderate risk, and high risk. It was confirmed that school refusal through negative reinforcement correlates negatively with health dimensions, also finding that a higher risk profile for school refusal is associated with lower levels of self-perceived health. Similarly, it was determined that the high-risk profile is the most maladaptive, with significantly lower data in four of the five self-perceived health dimensions that were evaluated. In conclusion, remaining in situations with no or moderate risk of school refusal due to negative reinforcement encourages higher levels of self-perceived health, while being at high risk of school refusal due to negative reinforcement is associated with worse self-perceived health.

Investigating school absenteeism and refusal among Australian children and adolescents using Apriori association rule mining

Identifying and determining the multitude of reasons behind school absences of students is often challenging. This study aims to uncover the hidden reasons for school absence in children and adolescents. The analysis is conducted on a national survey that includes 2967 Australian children and adolescents aged 11-17. The Apriori association rule generator of machine learning techniques and binary logistic regression are used to identify the significant predictors of school absences. Out of 2484, 83.7% (n = 2079) aged (11-17) years children and adolescents have missed school for various reasons, 42.28% (n = 879) are (11-15) years old, 24.52% (n = 609) and 16.9% (n = 420) are 16- and 17-years old adolescents respectively. A considerable proportion of adolescents, specifically 16.4% (n = 407) and 23.4% (n = 486) of 16 and 17 years old, respectively, have selected 'refused to say' as their reason for not attending school. It also highlights the negative outcomes associated with undisclosed reasons for school absence, such as bullying, excessive internet/gaming, reduced family involvement, suicide attempts, and existential hopelessness. The findings of the national survey underscore the importance of addressing these undisclosed reasons for school absence to improve the overall well-being and educational outcomes of children and adolescents.