Search Results (49)

Background: This study examined treatment outcomes for cannabis use disorder (CUD) among adolescents (12–17 years old) in the United States. Methods: Data from the 2018–2021 Treatment Episode Data Set-Discharges (TEDS-D) included 40,054 adolescents diagnosed with CUD. Descriptive statistics, Chi-square tests, and multivariable logistic regression assessed treatment outcomes and factors associated with treatment completion. Results: Only 36.8% of adolescents completed treatment. The most common reasons for not completing treatment were dropping out (28.4%) and transferring to another facility/program (17.0%). Males and Black non-Hispanic adolescents had lower odds of completing treatment (OR = 0.79, 95%CI: 0.75–0.84), while Hispanic (OR = 1.13, 95%CI: 1.08–1.18), Asian (OR = 1.56, 95%CI: 1.3–1.86) and Native Hawaiian/Pacific Islander adolescents (OR = 2.31, 95%CI: 2.04–2.61) had higher odds of completion compared to their White counterparts. Independent living arrangements, homelessness, arrests in the past 30 days and younger age (<15 years old) decreased the likelihood of treatment completion. Adolescents with co-occurring mental health and substance use disorders also had lower completion rates (OR = 0.79, 95%CI: 0.77–0.86). Referral from schools/employers and treatment settings were associated with a higher success, particularly with stays of 4–6 months and 7–12 months. Conclusion: This study highlights the need for targeted CUD treatment programs that support at-risk adolescents, especially those experiencing homelessness or facing legal issues. High dropout and transition rates suggest a need for continuity of care and program integration between facilities. Strengthening coordination among public health officials, community organizations, and stakeholders is essential to developing culturally responsive treatment interventions that address social determinants of health, substance use, and mental health in this vulnerable population. Full article

The aim of this study is to identify the key factors contributing to student dropout and to develop a predictive model that estimates the dropout risk of students based on their entry characteristics and enrolment registration data. Our analysis is based on the registration and academic data of 971 full-time and part-time bachelor’s students in five faculties, who were enrolled in the academic year 2021–2022 at the Latvia University of Life Sciences and Technologies (LBTU). The dropout analysis was done during the 3.5 years of study, when the students started their last semester in engineering and information technology, agriculture and food technology, economics and social sciences, and forest and environmental studies and when veterinary medicine students had completed more than half of their program of study. Survival analysis methods were used during the study. Students’ dropout risk in relation to gender, faculty, priority to study in the program, and secondary school performance (SM) was estimated using the Proportional hazard model (Cox model). The highest student dropout was observed during the first year of study. Secondary school performance was a significant predictor of students’ dropout risk; students with higher SM had a lower dropout risk (HR = 0.66, p < 0.05). As well, student dropout can be explained by faculty or study programme. Students in economics and social sciences were at lower dropout risk than the students from the other faculties. Results show the model’s concordance index was 0.59, and this indicates that additional or stronger predictors may be needed to improve model performance. Full article

This study investigates the dropout risk among first- and second-year high school students in the peripheral areas of Southern Italy, where the dropout rates are extremely high. It focuses on individual and relational factors associated with dropout, analyzing data from 645 students (M_age = 14.64) who completed a self-report questionnaire. The examined variables include self-efficacy, amotivation, future orientation, peer relationships, and students’ trust in teachers. Hierarchical regressions assessed the influence of grade levels on these dimensions. Our findings show a positive developmental trend in second-year students, including higher self-efficacy, better peer relationships, and reduced intentions to drop out. However, trust in teachers declines during this transition. Moreover, relationships with teachers show no significant improvement across grades. Therefore, this study underscores the importance of fostering trust between students and teachers as a protective factor against dropout. It also reveals the need for interventions targeting both students and the educational environment to improve teacher–student relationships and support students’ educational aspirations. By addressing these relational aspects, stakeholders can better mitigate dropout risks and promote school engagement during critical transitions in adolescence. Full article

School dropout in higher education remains a significant global challenge with profound socioeconomic consequences. To address this complex issue, educational institutions increasingly rely on business intelligence (BI) and related predictive analytics, such as machine learning and data mining techniques. This systematic review critically examines the application of BI and predictive analytics for analyzing and preventing student dropout, synthesizing evidence from 230 studies published globally between 1996 and 2025. We collected literature from the Google Scholar and Scopus databases using a comprehensive search strategy, incorporating keywords such as “business intelligence”, “machine learning”, and “big data”. The results highlight a wide range of predictive tools and methodologies, notably data visualization platforms (e.g., Power BI) and algorithms like decision trees, Random Forest, and logistic regression, demonstrating effectiveness in identifying dropout patterns and at-risk students. Common predictive variables included personal, socioeconomic, academic, institutional, and engagement-related factors, reflecting dropout’s multifaceted nature. Critical challenges identified include data privacy regulations (e.g., GDPR and FERPA), limited data integration capabilities, interpretability of advanced models, ethical considerations, and educators’ capacity to leverage BI effectively. Despite these challenges, BI applications significantly enhance institutions’ ability to predict dropout accurately and implement timely, targeted interventions. This review emphasizes the need for ongoing research on integrating ethical AI-driven analytics and scaling BI solutions across diverse educational contexts to reduce dropout rates effectively and sustainably. Full article

This article examines the audiovisual representation of violence during the armed takeover of the Ecuadorian television channel TC Television on 9 January 2024, an unprecedented event in the country’s recent media history. Employing a film analysis methodology, the study deconstructs the live broadcast by segmenting it into visual sequences and analyzing elements such as narrative content, shot composition, camera movement, sound design, and editing techniques. The interpretive phase includes narratological, iconic, and psychoanalytic readings. From a psychoanalytic perspective, the study explores the emotional impact of the broadcast on viewers, focusing on responses such as fear, anxiety, identification, projection, and the activation of psychological defense mechanisms. It also reflects on the broader sociocultural consequences of such representations of violence in public media. The article concludes by emphasizing the need for public investment in inclusive and high-quality education as a structural response to youth vulnerability, school dropout, and the risk of recruitment by organized criminal groups in Ecuador. Full article

School dropout is a global issue that compromises individual and societal well-being. Researchers in psychology emphasize that dropout often results from a prolonged erosion of bonds between individuals, schools, and society, especially in socioeconomically disadvantaged contexts. School trust, described as the “connective tissue” within the school system, fosters psychological well-being and is associated with self-esteem, self-efficacy, life satisfaction, and reduced depression. This study aimed to explore the interaction of various relational constructs related to school life, which could be used to improve student well-being and reduce the risk of dropout. A total of 645 high school students from impoverished and high-crime neighborhoods in Naples were involved in the cross-sectional study, investigating the role that school trust plays in relation to positive teaching, self-efficacy, and the sense of belonging. The results indicate that positive teaching significantly enhances the sense of school belonging through the mediating role of students’ trust in teachers. These findings highlight the crucial role of trust as a mediator in strengthening student–school relationships. Schools should prioritize fostering trust by promoting teacher transparency, consistency, and care. Such efforts can enhance students’ sense of belonging, ultimately mitigating dropout risk and restoring their connection with education. This systemic approach is especially vital in contexts with significant socioeconomic challenges. Full article

Community schools represent a transformative approach to addressing systemic inequities in public education by integrating academic, social, and health services to create equitable learning environments. This study investigated the long-term impact of community school programming at an urban elementary school on middle school academic outcomes and college readiness indicators. Utilizing a quasi-experimental design with rigorous inclusion criteria and propensity score matching, the researchers minimized the bias from baseline group differences to enhance the internal validity. The key findings indicate that students who attended the community school demonstrated significant increases in grade point average over time and were less likely to exhibit high school dropout risk factors compared to a demographically matched comparison group of students who did not attend a community school. A higher proportion of the community school students met college readiness benchmarks, underscoring the sustained impact of community school programming. These results align with the existing literature on the potential of community schools to mitigate academic disparities and highlight the importance of integrating holistic support into educational strategies. By demonstrating a robust methodological approach, this study contributes valuable evidence to guide policymakers and practitioners in scaling and optimizing community school models to advance educational equity and excellence. Full article

It is estimated that one in three students drop out of university by the end of the first year of study. Dropping out of university has significant consequences, not only for the student but also for the university and for society as a whole. A total of 1.154 Italian freshmen were involved in this study and were divided based on their intention to dropout from university. The intention to dropout was assessed using five questions, and motivation was assessed through the Academic Motivation Scale. Differences in socio-demographic factors, extra-curriculum activities, academic characteristics, and academic motivation between freshmen with low and high dropout risks were assessed for highlighting potential intervention for limiting dropout rates. The majority of the freshmen were female, from low-income families, had attended high school, and lived out of town; the most represented field of study was health professions. The results indicate that the variables increasing the likelihood of belonging to the high dropout risk group are as follows: unsatisfactory relationships with lecturers/professors and fellow students, low income, amotivation, and extrinsic motivation. This study underlines the importance of adopting new teaching approaches that include spaces and time dedicated to fostering relationships, supporting academic success, and promoting the psychosocial well-being of students. Full article

Graduating from secondary education for adolescents living in poverty is challenging. Strong entrepreneurial intention and student engagement among youth living in poverty often play a protective role in reducing school dropout and fostering school completion, which results in improved educational attainment. However, research on this topic is scarce. A total of 1135 adolescents took part in this cross-sectional study, 50.9% of which were females. On average, they were 16.4 years old. They were all upper secondary school students from ten public and private schools in Lebanon. They completed instruments measuring entrepreneurial intention and student engagement. This study explored the covariate associations between risk and promotive factors through four dimensions of entrepreneurial intention and two components of student engagement (cognitive and psychological engagement). It shows positive associations for entrepreneurial intention with both individual factors (age) and social factors (working mother and private school). Negative associations for student engagement were found in all (individual and social) factors with the exception of the father’s job, which did not present any association. The findings provide insight for policymaking to empower schools to promote school completion and educational attainment among these youth by providing policy initiatives and school-based interventions that target entrepreneurial exposure and engagement strengthening, hence meeting young people’s individual, family, and school community needs. Full article

In 2017, Loscalzo and Giannini introduced the new potential clinical condition of studyholism (or obsession toward study) and a comprehensive model including its possible antecedents and outcomes. Then, emphasizing the value of avoiding an aprioristic (addiction) framework in analyzing problematic overstudying, they suggested conducting research on this new construct to unveil its internalizing and/or externalizing nature while also avoiding the over-pathologizing of a common behavior such as studying. Seven years after the first publication about studyholism, growing evidence concerning its antecedents suggested that studyholism might be defined as an OCD-related disorder (or, more generally, as an internalizing disorder). Moreover, the research about its outcomes highlighted that it is a problem behavior deserving attention as it is associated with academic, psychological, physical, and social downsides. Therefore, this paper aims to review the scientific literature published concerning studyholism to illuminate if it might be conceptualized as an OCD-related disorder based on its symptomatology, antecedents, and impact on individuals’ academic, physical, and psychological functioning. Given that it is a new construct, it is of critical value to systematize the findings gathered until now as it can help scholars interested in students’ well-being to have a clear understanding concerning the importance of screening studyholism since childhood, as this will help favor academic success and well-being and reduce the risk for school dropout. Finally, this paper presents an agenda for future research on studyholism, and it highlights the importance of further analyzing problematic overstudying using different theoretical perspectives (such as the behavioral addiction conceptualization) to unveil its real nature. Full article

The dropout rate in underdeveloped and emerging countries is a pressing social issue, as highlighted by studies conducted by The Organization for Economic Co-operation and Development. This study compares five feature selection techniques to address this challenge and improve the automatic detection of dropout risk. The methodological design involves three distinct phases: data preparation, feature selection, and model evaluation utilizing machine learning algorithms. The results demonstrate that (1) the top features identified by feature selection techniques, i.e., those constructed through feature engineering, proved to be among the most effective in classifying student dropout; (2) the F-score of the best model increased by 5% with feature selection techniques; and (3) depending on the type of feature selection, the performance of the machine learning algorithm can vary, potentially increasing or decreasing based on the sensitivity of features with higher noise. At the same time, metaheuristic algorithms demonstrated significant precision improvements, but there was a risk of increasing errors and reducing recall. Full article

Background: Emotion regulation skills form part of many interventions for youth with internalizing and externalizing difficulties. This pilot study examines a prevention program delivered at school to improve adolescents’ emotion regulation skills, focusing on those at risk for mental health problems. Methods: Adolescents 12–18 years old were referred to a six-session group program by their school counselors, based on inclusion criteria related to family sociodemographic and mental health characteristics. Group sessions took place during school hours to facilitate participation and reduce dropout. The intervention targeted emotion regulation skills, drawing from central components of different cognitive behavioral approaches. To assess clinical outcomes, participants answered questionnaires before and after the program, which covered emotion regulation strategies, addictive behaviors, and internalizing and externalizing symptoms. The acceptability of the program was also assessed. Results: Emotion regulation skills improved after the program, and there was a significant reduction in internalizing and externalizing problems. The program was evaluated as useful by participants. Counsellors reported satisfaction with the program. Conclusions: Targeted emotion regulation skills training is a potentially useful transdiagnostic intervention to prevent mental health problems in youth. Bringing the intervention to the school setting and involving counsellors in referring at-risk students can facilitate uptake and reduce dropout. Full article

Machine learning (ML) has become integral in educational decision-making through technologies such as learning analytics and educational data mining. However, the adoption of machine learning-driven tools without scrutiny risks perpetuating biases. Despite ongoing efforts to tackle fairness issues, their application to educational datasets remains limited. To address the mentioned gap in the literature, this research evaluates the effectiveness of four bias mitigation techniques in an educational dataset aiming at predicting students’ dropout rate. The overarching research question is: “How effective are the techniques of reweighting, resampling, and Reject Option-based Classification (ROC) pivoting in mitigating the predictive bias associated with high school dropout rates in the HSLS:09 dataset?" The effectiveness of these techniques was assessed based on performance metrics including false positive rate (FPR), accuracy, and F1 score. The study focused on the biological sex of students as the protected attribute. The reweighting technique was found to be ineffective, showing results identical to the baseline condition. Both uniform and preferential resampling techniques significantly reduced predictive bias, especially in the FPR metric but at the cost of reduced accuracy and F1 scores. The ROC pivot technique marginally reduced predictive bias while maintaining the original performance of the classifier, emerging as the optimal method for the HSLS:09 dataset. This research extends the understanding of bias mitigation in educational contexts, demonstrating practical applications of various techniques and providing insights for educators and policymakers. By focusing on an educational dataset, it contributes novel insights beyond the commonly studied datasets, highlighting the importance of context-specific approaches in bias mitigation. Full article

School dropout is a pressing social problem that stems from systemic inadequacies in the education system and socio-economic background. The aim of this study was to analyze how the travel time and financial difficulties impact school dropout in satellite towns near Bucharest, the capital of Romania. Data on dropout rates in recent years were provided by the Ilfov County General School Inspectorate and were supplemented by 30 semi-structured interviews with the parents, caregivers, or the legal representatives of students who have dropped out of school or are at risk of dropping out. The study’s findings reveal significant correlations between the travel time to school, familial financial situation, and attitudes towards education, impacting dropout rates across various satellite towns. Addressing the challenge of school dropout promises societal improvement and empowers policymakers to enact more inclusive policies benefiting all members of society. Full article

School dropout may have important negative consequences for the individual as well as for society. Because school grades in lower secondary education are essential for the completion of upper secondary school, remotivating demotivated ninth graders with an increased risk of dropping out seems vital. This study focuses on the experiences of Norwegian ninth grade boys at a learning camp aimed at preventing school dropout through increasing school engagement, learning, and well-being before tenth grade. We interviewed 17 of the 29 participants in one particular camp to study their experiences and analyze how they were related to the theoretical underpinning of the camp. The participants described the learning camp as a motivation boost, focusing on experiences with academic progress and increased self-regulation, factors aligning with central theoretical underpinnings of the intervention. The participants placed “connecting with others”, as in peers and teachers, among the top two factors that contributed to their re-motivation, well-being, and academic progress. Full article