Background: According to the self‐enhancement perspective, self‐efficacy and self‐concept are shaped by prior achievement and have a crucial impact on future development. Their role in improving performance on challenging tasks, such as mathematical modelling (i.e., solving realistic problems mathematically), has barely been studied. Aims: We investigated patterns of self‐efficacy and self‐concept and their predictive effects on mathematical modelling while taking into account school grades as measure of prior achievement and reasoning to reveal cognitive and motivational effects on achievement. Sample: N = 279 secondary students in Grade 8 or 9 from 16 classes and 6 schools participated in the study. Method: The multi‐informant design consisted of teachers’ reports of school grades, students’ reports of self‐efficacy and self‐concept (questionnaire‐based), and assessment of students’ reasoning and mathematical modelling. Results: Using random‐intercept models, we found that the predictive effect of self‐efficacy on mathematical modelling withstood taking the school‐classroom‐related nested structure into account, whereas self‐concept lost its predictive value. Further, self‐efficacy fully mediated the effect of school grades on mathematical modelling. Conclusions: In line with the self‐enhancement perspective on self‐efficacy, our findings highlight the strength of motivational effects on mathematical modelling. When we take the nested structure into account, our results indicate an impact of school grades via self‐efficacy on mathematical modelling independent of students’ cognitive level or classroom. Given the diverse challenges such complex tasks present, important pedagogical and didactical recommendations, such as targeting the enhancement of students’ self‐efficacy by teachers and educational decision makers, can be drawn.

Background: Child welfare and juvenile justice placed youths show high levels of psychosocial burden and high rates of mental disorders. It remains unclear how mental disorders develop into adulthood in these populations. The aim was to present the rates of mental disorders in adolescence and adulthood in child welfare and juvenile justice samples and to examine their mental health trajectories from adolescence into adulthood.Methods: Seventy adolescents in shared residential care, placed by child welfare (n = 52, mean age = 15 years) or juvenile justice (n = 18, mean age = 17 years) authorities, were followed up into adulthood (child welfare: mean age = 25 years; juvenile justice: mean age = 27 years). Mental disorders were assessed based on the International Classification of Diseases 10th Revision diagnoses at baseline and at follow-up. Epidemiological information on mental disorders was presented for each group. Bivariate correlations and structural equation modeling for the relationship of mental disorders were performed.Results: In the total sample, prevalence rates of 73% and 86% for any mental disorder were found in adolescence (child welfare: 70%; juvenile justice: 83%) and adulthood (child welfare: 83%; juvenile justice: 94%) respectively. General psychopathology was found to be stable from adolescence into adulthood in both samples.Conclusions: Our findings showed high prevalence rates and a high stability of general psychopathology into adulthood among child welfare and juvenile justice adolescents in Swiss residential care. Therefore, continuity of mental health care and well-prepared transitions into adulthood for such individuals is highly warranted.

Mathematical literacy (ML) is considered central to the application of mathematical knowledge in everyday life and thus is found in many comparative international educational standards. However, there exists barely any evidence about predictors and outcomes of ML having a lasting effect on achievement in nonmathematical domains. We drew on a large longitudinal sample of N = 4001 secondary school students in Grades 5 to 9 and tested for effects of ML on later academic achievement. We took prior achievement in different domains (information and communication technology literacy, scientific literacy, reading comprehension, and listening comprehension), socioeconomic status, and gender into account and investigated predictive effects of math grade, mathematical self-concept, reasoning, and prior achievement on ML. Using structural equation models, we found support for the importance of integrating multiple predictors and revealed a transfer effect of ML on achievement in different school domains. The findings highlight the importance of ML for school curricula and lasting educational decisions.