This study meta-analytically examined the effect of treatment integrity on client outcomes of evidence-based interventions for juveniles with antisocial behavior. A total of 17 studies, from which 91 effect sizes could be retrieved, were included in the present 3-level meta-analysis. All included studies, to a certain level, adequately implemented procedures to establish, assess, evaluate and report the level of treatment integrity. A moderator analysis revealed that a medium-to-large effect of evidence-based interventions was found when the level of treatment integrity was high (d = 0.633, p < 0.001), whereas no significant effect was found when integrity was low (d = 0.143, ns). Treatment integrity was significantly associated with effect size even when adjusted for other significant moderators, indicating the specific contribution of high levels of treatment integrity to positive client outcomes. This implies that delivering interventions with high treatment integrity to youth with antisocial behavior is vital.
Worldwide, an increasing number of students seek private supplementary tutoring, known as ‘shadow education.’ Various studies report social class differences in the use of shadow education. High-SES families may invest in shadow education as a form of concerted cultivation, seeking to improve their children’s school achievement. In this study, we apply meta-analytic structural equation modeling to explore relationships between parental education, income, and the use of shadow education across nations and educational contexts. We find robust relationships between parental education, income and the use of shadow education. Moreover, we assess a mediating role of shadow education in the relationship between SES and achievement. Shadow education appears to fulfill a competitive function for privileged families who seek to secure advantage in educational competition. We conclude that educational research, particularly research concerned with inequality of opportunities, needs to take account of the progressively prominent position of shadow education in the educational landscape.
BACKGROUND: Prediction models and prognostic scores have been increasingly popular in both clinical practice and clinical research settings, for example to aid in risk-based decision making or control for confounding. In many medical fields, a large number of prognostic scores are available, but practitioners may find it difficult to choose between them due to lack of external validation as well as lack of comparisons between them.METHODS: Borrowing methodology from network meta-analysis, we describe an approach to Multiple Score Comparison meta-analysis (MSC) which permits concurrent external validation and comparisons of prognostic scores using individual patient data (IPD) arising from a large-scale international collaboration. We describe the challenges in adapting network meta-analysis to the MSC setting, for instance the need to explicitly include correlations between the scores on a cohort level, and how to deal with many multi-score studies. We propose first using IPD to make cohort-level aggregate discrimination or calibration scores, comparing all to a common comparator. Then, standard network meta-analysis techniques can be applied, taking care to consider correlation structures in cohorts with multiple scores. Transitivity, consistency and heterogeneity are also examined.RESULTS: We provide a clinical application, comparing prognostic scores for 3-year mortality in patients with chronic obstructive pulmonary disease using data from a large-scale collaborative initiative. We focus on the discriminative properties of the prognostic scores. Our results show clear differences in performance, with ADO and eBODE showing higher discrimination with respect to mortality than other considered scores. The assumptions of transitivity and local and global consistency were not violated. Heterogeneity was small.CONCLUSIONS: We applied a network meta-analytic methodology to externally validate and concurrently compare the prognostic properties of clinical scores. Our large-scale external validation indicates that the scores with the best discriminative properties to predict 3 year mortality in patients with COPD are ADO and eBODE.
