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BackgroundInternet interventions offer advantages that especially cancer survivors who suffer from fatigue could benefit from. Given the growing number of such patients, Internet interventions could supplement and strengthen currently available health care. ObjectiveThis paper describes the design and analysis plan that will be used to study 2 Internet interventions aimed at reducing severe fatigue in cancer survivors: a mobile ambulant activity feedback therapy supported through a weekly email by a physiotherapist and a weekly Web- and mindfulness-based cognitive therapy supported online by a psychologist. The data resulting from this trial will be used to (1) investigate the effectiveness, (2) investigate potential mediators of these interventions, and (3) explore participant characteristics that can predict the effect of these interventions. MethodsA 3-armed randomized controlled trial is proposed that compares both Internet interventions with an active control condition that solely consists of receiving psycho-educational emails. The intervention period is 9 weeks for all 3 conditions. Six months after baseline, participants in the control condition can choose to follow 1 of the 2 experimental Internet interventions. Outcomes are measured in terms of fatigue severity, mental health, and self-perceived work ability. All are Web-assessed at baseline, 2 weeks after the intervention period, and at 6 and 12 months after baseline. Fatigue severity, mindfulness, physical activity, expectations and credibility of the intervention, therapeutic working alliance, sleep quality, and sense of control over fatigue are assessed 3 times during the intervention period for identifying mediators of the interventions. Recruitment is performed nationally throughout the Netherlands through patient organizations and their websites, newspapers, and by informing various types of health professionals. All participants register at an open-access website. We aim at including 330 cancer survivors who have finished curative-intent cancer treatment at least 3 months previously, and have been suffering from severe fatigue ever since. All cancer types are included. A detailed analysis plan is described to address the research questions, which allows for individual variation, and fully exploits the longitudinal design. ResultsRecruitment started in April 2013 and will proceed until April 2015. ConclusionsThis paper describes a systematic trial design for studying 2 different interventions for chronic cancer-related fatigue in order to gain insight into the effectiveness and mediators of the interventions. This design will also be used to identify predictors for the interventions’ effect on fatigue. By publishing our hypotheses and analysis plan before completion of data collection, this paper is a first step in reporting on this trial comprehensively. Trial RegistrationThe Netherlands National Trial Register (NTR3483). (Archived by WebCite at http://www.webcitation.org/6NWZqon3o).

BackgroundPosttraumatic stress disorder (PTSD) develops in 10-20% of injury patients. We developed a novel, self-guided Internet-based intervention (called Trauma TIPS) based on techniques from cognitive behavioral therapy (CBT) to prevent the onset of PTSD symptoms. ObjectiveTo determine whether Trauma TIPS is effective in preventing the onset of PTSD symptoms in injury patients. MethodsAdult, level 1 trauma center patients were randomly assigned to receive the fully automated Trauma TIPS Internet intervention (n=151) or to receive no early intervention (n=149). Trauma TIPS consisted of psychoeducation, in vivo exposure, and stress management techniques. Both groups were free to use care as usual (nonprotocolized talks with hospital staff). PTSD symptom severity was assessed at 1, 3, 6, and 12 months post injury with a clinical interview (Clinician-Administered PTSD Scale) by blinded trained interviewers and self-report instrument (Impact of Event Scale—Revised). Secondary outcomes were acute anxiety and arousal (assessed online), self-reported depressive and anxiety symptoms (Hospital Anxiety and Depression Scale), and mental health care utilization. Intervention usage was documented. ResultsThe mean number of intervention logins was 1.7, SD 2.5, median 1, interquartile range (IQR) 1-2. Thirty-four patients in the intervention group did not log in (22.5%), 63 (41.7%) logged in once, and 54 (35.8%) logged in multiple times (mean 3.6, SD 3.5, median 3, IQR 2-4). On clinician-assessed and self-reported PTSD symptoms, both the intervention and control group showed a significant decrease over time (P

The present study investigated (1) how social relationships with teachers and peers and self-esteem of students with social-emotional and behavioral difficulties (SEBD) in inclusive regular education (regular schools) and students with SEBD in exclusive special education (special schools) develop over time in comparison with each other and in comparison with their typically developing peers and (2) whether factors--present before students with SEBD received special education services--predicted social-emotional development in either educational setting. Thirty-six "students with SEBD in regular schools," 15 "students with SEBD in special schools," and 1,270 "typically developing" peers participated. We collected data when students with SEBD resided in regular education without additional support, and we followed the development of students with SEBD for 1.5 years with three additional measurements in either school setting. Data of typically developing peers were collected when they resided in a classroom of a participating student with SEBD. Using Bayesian statistics, we found that students with SEBD in special schools had more conflictual relationships with their teachers than typically developing peers, but these relationships improved over time. Students with SEBD in regular schools were less accepted among peers than typically developing students and peer acceptance was stable over time for all three groups. Self-esteem and development in self-esteem over time did not differ between groups. The current study shows that students with SEBD show different developmental trajectories in regular or special schools and that it is difficult to predict their social-emotional development by factors present before students with SEBD received special education services.

Helping university students develop productive views of what characterizes good scientific research and scientific knowledge is an important objective of science education. However, many studies show that students' views of the nature of science (NOS) do not become more informed or even become less informed after learning activities that engage students in scientific practice. This could mean that the way we teach students to conduct scientific research might unintentionally strengthen or bring about uninformed views of NOS. In this article, we argue that scientific discourse (how language is used in science) might play a role in this relation between learning scientific inquiry and uninformed views of NOS. We argue that there could be implicit notions relating to NOS underlying scientific discourse that uninformed readers might translate to uninformed views of NOS. We call these implicit notions underlying scientific language use "epistemological notions." In this article, we further define this construct of epistemological notions, contrast it with explicit views of NOS and other related constructs, explain how we think epistemological notions might affect explicit views of NOS, and present a framework we developed to characterize them in scientific language use. It is a descriptive and interpretative analysis framework which combines, optimizes, and extends several text analysis methods, discourse analysis, and reflexive thematic analysis. We provide a guide to use the framework and point out quality criteria. We finish by advocating the framework for educational researchers interested in developing instructional interventions during which learning about science is combined with explicit reflection on NOS. Identifying epistemological notions in scientific language use could provide starting points for these activities by making explicit the translation from NOS understanding to scientific practice and vice versa. [ABSTRACT FROM AUTHOR]

This study examined which factors were related to placement choices for inclusive regular education or exclusive special education for Dutch students with social, emotional, and behavioral difficulties (SEBD). Students aged 6-11 in three subgroups participated: 45 included and 17 excluded students with SEBD (i.e., educated in regular education classrooms and in a separate setting exclusively for students with SEBD, respectively) and 772 typically developing peers. Before placement choices had been made, we collected data from students and teachers during classroom surveys and individual testing sessions with students with SEBD and from application files. Using Bayesian statistics, we found that included and excluded students with SEBD were similar in student functioning prior to placement, whereas teachers of included students had lower self-efficacy and more positive attitudes toward inclusion than teachers of excluded students. Furthermore, included and excluded students perceived their social-- emotional functioning more negatively than typically developing peers. Hence, although considered essential by existing policies, placement choices may not depend on student functioning, but teacher factors may play a role.

Gilles de la Tourette's syndrome (GTS) is a disorder in which obsessive-compulsive (OC), Attention Deficit Hyperactivity Disorder (ADHD) and autism symptoms occur in up to 60% of patients, suggesting shared etiology. We explored the phenotypic structure of tic, OC, ADHD, and autism symptoms as measured by the YGTSS,Y-BOCS,CAARS and AQ, in 225 GTS patients and 371 family members. First, Confirmatory Factor Analyses (CFA) were performed on the symptom structure of each separate symptom scale. Second, the symptom dimensions derived from each scale were combined in one model, and correlations between them were calculated. Using the correlation matrix, Exploratory Factor Analyses (EFA) were performed on the symptom dimensions across the scales. EFA revealed a five factor structure: tic/aggression/symmetry; OC symptoms/compulsive tics/ numbers and patterns; ADHD symptoms; autism symptoms; and hoarding/inattention symptoms. The results are partly in line with the traditional categorical boundaries of the symptom scales used, and partly reveal a symptom structure that cuts through the diagnostic categories. This phenotypic structure might more closely reflect underlying etiologies than a structure that classically describes GTS patients according to absence or presence of comorbid OCD, ADHD and autism, and might inform both future genetic and treatment studies.

BACKGROUND: After traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here), the risk to develop posttraumatic stress disorder (PTSD) is approximately 10% (Breslau & Davis, 1992). Latent Growth Mixture Modeling can be used to classify individuals into distinct groups exhibiting different patterns of PTSD (Galatzer-Levy, 2015). Currently, empirical evidence points to four distinct trajectories of PTSD patterns in those who have experienced burn trauma. These trajectories are labeled as: resilient, recovery, chronic, and delayed onset trajectories (e.g., Bonanno, 2004; Bonanno, Brewin, Kaniasty, & Greca, 2010; Maercker, Gäbler, ONeil, Schützwohl, & Müller, 2013; Pietrzak et al., 2013). The delayed onset trajectory affects only a small group of individuals, that is, about 4-5% (ODonnell, Elliott, Lau, & Creamer, 2007). In addition to its low frequency, the later onset of this trajectory may contribute to the fact that these individuals can be easily overlooked by professionals. In this special symposium on Estimating PTSD trajectories (Van de Schoot, 2015a), we illustrate how to properly identify this small group of individuals through the Bayesian estimation framework using previous knowledge through priors (see, e.g., Depaoli & Boyajian, 2014; Van de Schoot, Broere, Perryck, Zondervan-Zwijnenburg, & Van Loey, 2015). METHOD: We used latent growth mixture modeling (LGMM) (Van de Schoot, 2015b) to estimate PTSD trajectories across 4 years that followed a traumatic burn. We demonstrate and compare results from traditional (maximum likelihood) and Bayesian estimation using priors (see, Depaoli, 2012, 2013). Further, we discuss where priors come from and how to define them in the estimation process. RESULTS: We demonstrate that only the Bayesian approach results in the desired theory-driven solution of PTSD trajectories. Since the priors are chosen subjectively, we also present a sensitivity analysis of the Bayesian results to illustrate how to check the impact of the prior knowledge integrated into the model. CONCLUSIONS: We conclude with recommendations and guidelines for researchers looking to implement theory-driven LGMM, and we tailor this discussion to the context of PTSD research.

Bayesian confirmatory factor analysis (CFA) offers an alternative to frequentist CFA based on, for example, maximum likelihood estimation for the assessment of reliability and validity of educational and psychological measures. For increasing sample sizes, however, the applicability of current fit statistics evaluating model fit within Bayesian CFA is limited. We propose, therefore, a Bayesian variant of the root mean square error of approximation (RMSEA), the BRMSEA. A simulation study was performed with variations in model misspecification, factor loading magnitude, number of indicators, number of factors, and sample size. This showed that the 90% posterior probability interval of the BRMSEA is valid for evaluating model fit in large samples (N= 1,000), using cutoff values for the lower (<0.05) and upper limit (<0.08) as guideline. An empirical illustration further shows the advantage of the BRMSEA in large sample Bayesian CFA models. In conclusion, it can be stated that the BRMSEA is well suited to evaluate model fit in large sample Bayesian CFA models by taking sample size and model complexity into account.

The ability of a scientific discipline to build cumulative knowledge depends on its predominant method of data analysis. A steady accumulation of knowledge requires approaches which allow researchers to consider results from comparable prior research. Bayesian statistics is especially relevant for establishing a cumulative scientific discipline, because the incorporation of background (or prior) knowledge is fundamentally anchored in its basic principles. The aim of the current systematic review is to provide insights into the current state of methodological affairs in educational research, with a focus on Bayesian statistics and the use of prior information. An analysis of publication histories of the 224 educational journals currently listed in the Thomson Reuters Journal Citation Report 2015 indicates that Bayesian statistics is primarily used to solve methodological problems, rather than used to build cumulative knowledge based on a combination of study results with comparable prior research. The utilisation of Bayesian statistics is motivated by its flexibility: models are estimated which would not be estimable with frequentist approaches, thus expanding the methodological repertoire of educational researchers and producing knowledge which otherwise would not have been available. Lastly, the predominant use of noninformative prior distributions indicates that one of the biggest advantages of Bayesian statistics, namely the combination of study results with comparable prior research, remains underutilised in educational research. Practical implications of these findings for educational research are illustrated and discussed.

Featured Application: Increasing reproducibility for active learning-aided systematic screening is essential and our checklist can be used to evaluate reproducibility and data efficiency of software. In the screening phase of a systematic review, screening prioritization via active learning effectively reduces the workload. However, the PRISMA guidelines are not sufficient for reporting the screening phase in a reproducible manner. Text screening with active learning is an iterative process, but the labeling decisions and the training of the active learning model can happen independently of each other in time. Therefore, it is not trivial to store the data from both events so that one can still know which iteration of the model was used for each labeling decision. Moreover, many iterations of the active learning model will be trained throughout the screening process, producing an enormous amount of data (think of many gigabytes or even terabytes of data), and machine learning models are continually becoming larger. This article clarifies the steps in an active learning-aided screening process and what data is produced at every step. We consider what reproducibility means in this context and we show that there is tension between the desire to be reproducible and the amount of data that is stored. Finally, we present the RDAL Checklist (Reproducibility and Data storage for Active Learning-Aided Systematic Reviews Checklist), which helps users and creators of active learning software make their screening process reproducible. [ABSTRACT FROM AUTHOR]