Healing after traumatic events: Aligning interventions with cultural background and religious and spiritual beliefs

Historically, disaster response efforts have focused primarily on basic physical survival and safety—addressing medical emergencies and ensuring adequate shelter and supplies of food and water. However, in addition to physical survival, relief efforts must also consider strategies to promote emotional healing and psychological well-being. In particular, those who intervene must consider the acceptability of intervention strategies, taking into account survivors' cultural background, religious customs, and spiritual beliefs. This article offers recommendations to better align school-based therapeutic interventions with children's religious and spiritual beliefs.     

A Meta-Analysis of Single-Case Research on Mathematics Word Problem-Solving Interventions for English Learners with Learning Disabilities and Mathematics Difficulties

This single-case meta-analysis is the first to provide a quantitative synthesis of the published literature on mathematics word problem-solving intervention studies for English learners with learning disabilities and mathematics difficulties. A total of ten single-subject studies were included for analysis. The current study investigated the magnitude of the effect of mathematics interventions targeting the improvement of students’ mathematics word problem-solving performance, and analyzed variables that moderated this effect. Results indicate a moderate overall effect size of 0.81 (95% CI [0.71, 0.90]) on word problem-solving performance for English learners with learning disabilities and mathematics difficulties. Moderator variables such as the implementer of the interventions, instructional focus, and word problem-solving content significantly moderated the mathematics intervention effects.     

Self-Determination to Increase Oral Reading Fluency Performance: Pilot and Replication Single-Case Design Studies

We report findings from multiple baseline design studies examining the effects of a program (“Data Mountain”) to improve the oral reading fluency (ORF) performance of 12 third-grade students with significant reading difficulties. The Data Mountain program guided students through self-monitoring, goal setting, and motivation training related to their reading performance. In the pilot study, these components were introduced across two experimental phases (self-monitoring and goal setting, followed by motivation training) to observe potential additive effects. In the replication study, the full Data Mountain program was introduced in one experimental phase. Between-case standardized mean difference effect sizes yielded moderate effects across studies (ES = 0.53, 0.63). Visual analyses of data revealed that students demonstrated increased levels of words read correctly per minute (wpm). In the pilot study, students’ fluency increased to an average of 22 wpm in the self-monitoring and goal-setting phase, and to a further 9 wpm in the motivation training phase. In the replication study, students increased an average of 18 wpm with the Data Mountain program. These results provide evidence to suggest that self-determination and motivation training have the potential to support the ORF performance of students with significant reading difficulties in the elementary grades.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Beyond the Nuclear Family: Children’s Social Networks and Depressive Symptomology

This study identified social network profiles among children, determined whether profiles vary across sociodemographic characteristics, and investigated links between childhood profiles and depressive symptomology using three waves of data spanning 23 years (Wave 1, 1992 Metro-Detroit representative sample: n = 193, ages 8–12). Latent profile analysis revealed three profiles: Close Family (22%), Varied Family (57%), Friend and Family (21%). White children were more likely than Black children to be in the Friend and Family profile. Children in the Close Family profile reported the lowest levels of depressive symptomology in childhood, but exhibited the steepest increase over time. Findings suggest that close, family centric networks are protective in childhood, but less so as children develop into adulthood, highlighting the importance of considering developmental perspectives.     

The Development and Evaluation of the Intersectional Privilege Screening Inventory for Use With Counselors-in-Training

As counselor preparation programs are compelled to demonstrate student social and cultural competence, valid inventories are needed to measure development consistent with contemporary standards. The Intersectional Privilege Screening Inventory was created for this purpose, was assessed using 4 types of validity evidence, and has results supporting its use in student development.     

Neuroanatomy Learning: Augmented Reality vs. Cross-Sections

Neuroanatomy education is a challenging field which could benefit from modern innovations, such as augmented reality (AR) applications. This study investigates the differences on test scores, cognitive load, and motivation after neuroanatomy learning using AR applications or using cross-sections of the brain. Prior to two practical assignments, a pretest (extended matching questions, double-choice questions and a test on cross-sectional anatomy) and a mental rotation test (MRT) were completed. Sex and MRT scores were used to stratify students over the two groups. The two practical assignments were designed to study (1) general brain anatomy and (2) subcortical structures. Subsequently, participants completed a posttest similar to the pretest and a motivational questionnaire. Finally, a focus group interview was conducted to appraise participants’ perceptions. Medical and biomedical students (n = 31); 19 males (61.3%) and 12 females (38.7%), mean age 19.2 ± 1.7 years participated in this experiment. Students who worked with cross-sections (n = 16) showed significantly more improvement on test scores than students who worked with GreyMapp-AR (P = 0.035) (n = 15). Further analysis showed that this difference was primarily caused by significant improvement on the cross-sectional questions. Students in the cross-section group, moreover, experienced a significantly higher germane (P = 0.009) and extraneous cognitive load (P = 0.016) than students in the GreyMapp-AR group. No significant differences were found in motivational scores. To conclude, this study suggests that AR applications can play a role in future anatomy education as an add-on educational tool, especially in learning three-dimensional relations of anatomical structures.     

Power training in elite young soccer players: Effects of using loads above or below the optimum power zone

ABSTRACT

This study aimed to examine the effects of two jump squat (JS) training programs involving different loading ranges in under-20 soccer players during a preseason period. Twenty-three elite young soccer players performed sprint speed (at 5-, 10-, and 20-m), change-of-direction (COD) speed, JS peak-power (PP), and countermovement jump (CMJ) tests pre and post four weeks of training. Athletes were pair-matched in two groups according to their optimum power loads (OPL) as follows: lower than OPL (LOPL; athletes who trained at a load 20% lower than the OPL) and higher than OPL (HOPL; athletes who trained at a load 20% higher than the OPL). Magnitude-based inferences were used to compare pre- and post-training measures. Meaningful increases in the PP JS were observed for both groups. Likely and possible improvements were observed in the 5- and 10-m sprint velocity in the LOPL group. Meanwhile, possible and likely improvements were observed in the CMJ, 5- and 10-m sprint velocity, and COD speed in the HOPL group. Overall, both training schemes induced positive changes in athletic performance. Soccer coaches and sport scientists can implement the JS OPL-based training schemes presented here, either separately or combined, to improve the physical performance of youth soccer players.