Adjustment to college and prediction of depression during post-secondary transition

Many studies have reported an increase in mental health problems during post-secondary transition, often originating from high school years. The present study examined how depressive symptoms during the 2 years following the post-secondary transition could be predicted by, on the one hand, school performance, externalized and learning difficulties, and depressive symptoms before the post-secondary transition, and on the other hand, personal factors, family functioning, and adjustment to college after the transition. From a sample of 438 participants (M = 16.20, SD = 0.87) at time point 1, an integrated model was elaborated using structural equation modeling. The statistical analyses showed that the five constructs fit the data well. The path coefficients showed a negative relationship between externalized and learning problems as perceived by the teacher (ELPT) before the transition and academic performance (AP). Personal characteristics (PC) negatively predicted academic adjustment (AC) over time, whereas the path coefficients between the family factor (FF) and AC were not significant over time. ELPT and AP negatively predicted depression at time point 1. At time points 2 and 3, PC positively predicted depression, and depressive symptoms were positively related over time. The percentage of variance accounted for by the depressive symptoms increased over time.     

A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

On the Application of the Three-Step Approach to Growth Mixture Models

This series of simulation studies evaluate, in the context of applied research settings, the impact of the parameterization of the covariance structure of the growth mixture model (GMM) on the regression coefficient and standard error estimates in the 3-step method. The results show that the 1-step approach performs better than the 3-step method across the simulation studies. However, the performance of the 3-step method depends slightly or importantly on the parameterization of the GGM from the first step, on the inclusion or not of the predictor at the first step of the analysis, on the population model, and on the type (i.e., logit vs. linear) and size of the regression coefficient estimates.     

A multidimensional model of school dropout from an 8-year longitudinal study in a general high school population

This study tests an empirical multidimensional model of school dropout, using data collected in the first year of an 8-year longitudinal study, with first year high school students aged 12–13 years. Structural equation modeling analyses show that five personal, family, and school latent factors together contribute to school dropout identified at 19 years of age: poor parent–teenager relationships, youth depression and family difficulties, negative classroom climate, negative school interactions, and poor academic achievement. This model increases our understanding of the dropout process in the general population and has direct implications for the development of high school dropout prevention programs.     

Estimating running spatial and temporal parameters using an inertial sensor

An inertial measurement unit (IMU) is widely considered to be an economical alternative to capture human motion in daily activities. Use of an IMU for clinical study, rehabilitation, and in the design of orthoses and prostheses has increased tremendously. However, its use in defining running gait is limited. This study presents a practical method to estimate running spatial and temporal parameters using an inertial sensor by placing it on a shoe. A combination of a zero-crossing method and thresholding is used to identify foot-strike and foot-off based on foot acceleration during running. Stride time, ground contact time and flight time can then be identified. An off-phase segmentation algorithm is applied to estimate stride length and running speed. These two parameters are commonly used to evaluate running efficiency and to differentiate elite runners. This study found that an IMU can estimate foot-strike and foot-off with average absolute time differences of 2.60–6.04 and 2.61–16.28 ms, respectively. Stride time was estimated with error between ? 4.04 and 0.33 ms. Stride length and running speed were estimated with maximum average errors of 45.97 mm and 0.41 km/h.     

International trends in health science librarianship part 15: West Africa (Ghana,Nigeria, Senegal)

This is the 15th in a series of articles exploring international trends in health science librarianship in the 21st century. It is the third of four articles pertaining to different regions in the African continent. The present issue focuses on countries in West Africa (Ghana, Nigeria and Senegal). The next feature column will investigate trends in North Africa. JM     

Two-dimensional ferromagnetic superlattices

Mechanically exfoliated two-dimensional ferromagnetic materials (2D FMs) possess long-range ferromagnetic order and topologically nontrivial skyrmions in few layers. However, because of the dimensionality effect, such few-layer systems usually exhibit much lower Curie temperature (T_C) compared to their bulk counterparts. It is therefore of great interest to explore effective approaches to enhance their T_C, particularly in wafer-scale for practical applications. Here, we report an interfacial proximity-induced high-T_C 2D FM Fe₃GeTe₂ (FGT) via A-type antiferromagnetic material CrSb (CS) which strongly couples to FGT. A superlattice structure of (FGT/CS)_n, where n stands for the period of FGT/CS heterostructure, has been successfully produced with sharp interfaces by molecular-beam epitaxy on 2-inch wafers. By performing elemental specific X-ray magnetic circular dichroism (XMCD) measurements, we have unequivocally discovered that T_C of 4-layer Fe₃GeTe₂ can be significantly enhanced from 140 K to 230 K because of the interfacial ferromagnetic coupling. Meanwhile, an inverse proximity effect occurs in the FGT/CS interface, driving the interfacial antiferromagnetic CrSb into a ferrimagnetic state as evidenced by double-switching behavior in hysteresis loops and the XMCD spectra. Density functional theory calculations show that the Fe-Te/Cr-Sb interface is strongly FM coupled and doping of the spin-polarized electrons by the interfacial Cr layer gives rise to the T_C enhancement of the Fe₃GeTe₂ films, in accordance with our XMCD measurements. Strikingly, by introducing rich Fe in a 4-layer FGT/CS superlattice, T_C can be further enhanced to near room temperature. Our results provide a feasible approach for enhancing the magnetic order of few-layer 2D FMs in wafer-scale and render opportunities for realizing realistic ultra-thin spintronic devices.