The Impact of Community Diversity and Consolidated Inequality on Dropping Out of High School

Abstract: Data from the National Education Longitudinal Study were combined with census data at the zip code level to examine the impact of neighborhood racial and ethnic diversity and consolidated inequality, in addition to individual, family, and school factors, on the likelihood of dropping out of high school. Results indicate that while the effects for diversity and consolidated inequality did not support the stated hypotheses, main effects for family risk and prior academic achievement were significant and in the stated direction. Also, when controlling for individual, family, school, and neighborhood characteristics, African Americans were less likely than White students to drop out of school. Implications for contextual effects research and educational outcomes are discussed.     

Patient demographics and psychological functioning as predictors of unilateral termination of psychodynamic therapy

Approximately one in five patients drops out of treatment before its completion. Little is known about consistent predictors of dropout, and most studies focus on patients’ demographic characteristics. A mass of information is collected daily at intake in clinical practice. Based on psychodynamic theoretical conceptualizations and accumulative clinical experience, this information may help predict dropout, and thereby expand the empirically based predictors of dropout. Objective: The present study aims at bridging between scientific research and clinical practice by investigating potential predictors of unilateral termination collected at intake, before therapy, in addition to predictors already identified in the literature. Method: The study was based on data from 413 patients from a university consulting center. Each patient completed a pre-intake questionnaire collecting demographic information, and underwent an interview conducted by a professional intaker. Results: Results indicate that the consistent predictors described in the literature, education, and age, were related to unilateral termination rates. Additionally, lower intrapsychic functionality, as evaluated by the intakers, was also found to contribute uniquely to higher unilateral termination rates. Conclusion: This finding attests to the unique value of professional evaluations of patients’ intrapsychic functionality, frequently conducted in clinical practice, to detect patients at risk of unilateral termination of treatment.     

Improving psychotherapy effectiveness by addressing the problem of premature termination: Introduction to a special section

Premature termination appears to be a consistent problem in psychotherapy, showing up across client, treatment, and therapist types. As psychotherapy researchers and practitioners, it is important that we gain a better understanding of this negative psychotherapy event and identify methods for reducing its occurrence. This article introduces a special section on premature termination in psychotherapy. In addition to briefly introducing the articles contained in the special section, this article offers suggestions for future research on the topic of premature termination.     

Optimal dropout policies for networked control systems

By realizing the feedback paths over communication networks, we get a class of networked control systems (NCSs), where the network's quality‐of‐service (QoS) is commonly characterized by the average dropout rate of feedback data packets. The control performance of an NCS however, is determined not only by the average dropout rate but also by the dropout pattern of feedback data packets. This paper provides a systematic way to determine the optimal dropout pattern (policy) under a given average dropout rate, where the performance is measured by the output signal power under an exogenous white noise. By modeling the finite‐memory dropout policies with the general Markov chain, this paper formulates the optimal dropout policy design into the optimization of parameters of a dropout Markov chain. That optimization is first solved by an augmented Lagrangian gradient method, which may be stuck at local optima because of the problem's non‐convexity. To compensate this weakness, we apply the branch‐and‐bound method to the optimization whose constraints are bilinear. The branch‐and‐bound method can approach the global optimal solution with any desired tolerance in finite steps. The obtained optimal dropout policy may be interpreted as a network's QoS constraint whose enforcement provides a hard guarantee on the control system's performance. An example is used to illustrate the effectiveness of the achieved results. Copyright © 2012 John Wiley & Sons, Ltd.     

Prediction of spurious HLA class II typing results using probabilistic classification

While modern high-throughput sequence-based HLA genotyping methods generally provide highly accurate typing results, artefacts may nonetheless arise for numerous reasons, such as sample contamination, sequencing errors, read misalignments, or PCR amplification biases. To help detecting spurious typing results, we tested the performance of two probabilistic classifiers (binary logistic regression and random forest models) based on population-specific genotype frequencies. We trained the model using high-resolution typing results for HLA-DRB1, DQB1, and DPB1 from large samples of German, Polish and UK-based donors. The high predictive capacity of the best models replicated both in 10-fold cross-validation for each gene and in using independent evaluation data (AUC 0.820–0.893). While genotype frequencies alone provide enough predictive power to render the model generally useful for highlighting potentially spurious typing results, the inclusion of workflow-specific predictors substantially increases prediction specificity. Low initial DNA concentrations in combination with low-volume PCR reactions form a major source of stochastic error specific to the Fluidigm chip-based workflow at DKMS Life Science Lab. The addition of DNA concentrations as a predictor variable thus substantially increased AUC (0.947–0.959) over purely frequency-based models.     

Evaluating Factors Contributing to Dropout in a Large Peritoneal Dialysis Program

BackgroundThe low prevalence of peritoneal dialysis (PD) (9%) vs. hemodialysis (HD) (88.2%) is partly due to patient dropout from therapy.MethodsThis retrospective study identified patients who withdrew from PD between 2016 and 2018 in our program. We evaluated all other factors as controllable losses. Analysis included time on therapy at dropout (very early, early or late) and method of initiation (HD to PD conversion, unplanned PD, or planned start).ResultsEighty-three patients enrolled into our PD program. 27 dropped out; 24 were due to controllable factors, 3 due to death, with a median age at dropout of 52 years old. We determined psychosocial factors (PF) to be the largest controllable factor influencing dropout; contributing a 63% rate among all controllable factors. When considering time until dropout, 100% of very early dropout patients and 50% of late dropout patients did so due to PF. Among early dropout patients 67% dropped out due to other medical reasons. The mean time to dropout for PF, other, and infection (INF) were 13, 26, and 33 months, respectively. When considering type of initiation, we found PF to be the largest attributable factor with 50% of unplanned, 100% of planned, and 50% of conversions stopping therapy.ConclusionsOur study indicates that the primary reason for controllable loss from therapy was secondary to PF regardless of the time on therapy or the method of initiation to therapy.     

Parent–child attachment,academic performance and the process of high-school dropout: a narrative review

Poor academic performance is a strong predictor of school dropout. Researchers have tried to disentangle variables influencing academic performance. However, studies on preschool and early care variables are seldom examined when explaining the school dropout process. We reviewed the literature on the relationship between caregiver–child attachment and academic performance, including attachment studies from preschool years, seeking out potential contributions to academic performance and the dropout process. The review was organized according to a model of four main mediating hypotheses: the attachment-teaching hypothesis, the social network hypothesis, the attachment-cooperation hypothesis, and the attachment self-regulation hypothesis. The results of the review are summed up in a model. There is some support for all four hypotheses. The review indicates that attachment and early care contribute substantially to dropout and graduation processes. Mediation effects should be given far more attention in future research.