Spiritual Versus Religious Identity: A Necessary Distinction in Understanding Clinicians’ Behavior and Attitudes Toward Clinical Practice and Medical Student Teaching in This Realm

Social sciences view spirituality and religion separately; medicine views them together. We identified distinctions regarding clinical practice and teaching among clinician educators based on their self-identified spirituality versus religiosity. We emailed a 24-item survey on spiritual/religious (S/R) issues to clinician educators (n = 1,067) at our institution. Three summary scales were created. Responses to statements, ‘I consider myself to be spiritual’ and ‘I consider myself to be religious’ generated four comparison groups: ‘spiritual only,’ ‘religious only,’ ‘both spiritual and religious’ and ‘neither.’ Analyses employed ANOVA and T tests. A total of 633 (59 %) surveys were completed. Four percentage self-identified as ‘religious only’; remaining respondents divided evenly, about 30 % into each of the other categories. Groups differed from one another on all summary scales (p < .0001). Using T tests, the ‘spiritual only’ group differed from the ‘religious only’ group regarding teaching. The ‘spiritual and religious’ group had the highest mean ratings for all summary scales. The ‘neither’ and ‘religious only’ group had the lowest mean ratings. Clinicians’ spiritual versus religious identity is associated with differences in behavior/attitudes regarding S/R toward clinical practice and medical student teaching. These findings elucidate opportunities for faculty development to explore effects of beliefs on behavior and attitudes within this realm.     

Using generalized additive (mixed) models to analyze single case designs

This article shows how to apply generalized additive models and generalized additive mixed models to single-case design data. These models excel at detecting the functional form between two variables (often called trend), that is, whether trend exists, and if it does, what its shape is (e.g., linear and nonlinear). In many respects, however, these models are also an ideal vehicle for analyzing single-case designs because they can consider level, trend, variability, overlap, immediacy of effect, and phase consistency that single-case design researchers examine when interpreting a functional relation. We show how these models can be implemented in a wide variety of ways to test whether treatment is effective, whether cases differ from each other, whether treatment effects vary over cases, and whether trend varies over cases. We illustrate diagnostic statistics and graphs, and we discuss overdispersion of data in detail, with examples of quasibinomial models for overdispersed data, including how to compute dispersion and quasi-AIC fit indices in generalized additive models. We show how generalized additive mixed models can be used to estimate autoregressive models and random effects and discuss the limitations of the mixed models compared to generalized additive models. We provide extensive annotated syntax for doing all these analyses in the free computer program R.     

School System Evaluation by Value Added Analysis Under Endogeneity

Value added is a common tool in educational research on effectiveness. It is often modeled as a (prediction of a) random effect in a specific hierarchical linear model. This paper shows that this modeling strategy is not valid when endogeneity is present. Endogeneity stems, for instance, from a correlation between the random effect in the hierarchical model and some of its covariates. This paper shows that this phenomenon is far from exceptional and can even be a generic problem when the covariates contain the prior score attainments, a typical situation in value added modeling. Starting from a general, model-free definition of value added, the paper derives an explicit expression of the value added in an endogeneous hierarchical linear Gaussian model. Inference on value added is proposed using an instrumental variable approach. The impact of endogeneity on the value added and the estimated value added is calculated accurately. This is also illustrated on a large data set of individual scores of about 200,000 students in Chile.     

Three Dimensions of Oppositionality in Autism Spectrum Disorder

In autism spectrum disorder (ASD), symptoms of oppositional defiant disorder (ODD) are common but poorly understood. DSM-5 has adopted a tripartite model of ODD, parsing its features into ‘angry and irritable symptoms’ (AIS), ‘argumentative and defiant behavior’ (ADB) and ‘vindictiveness’. This was based on findings in non-autistic populations that each of these dimensions of oppositionality has a distinct constellation of associations with internalising and externalising psychopathology. We applied the tripartite DSM-5 ODD model to ASD to test its generalisability beyond non-ASD populations; and to elucidate the nature of ODD symptoms in ASD. Participants were 216 verbally-fluent young people (mean age?=?9.6 years, range 3.0 to 16.2 years, 82 % male) with ASD. Cross-sectional parent-and teacher-report data were analysed using bootstrap multiple regression to test the following predictions, derived from studies of non-ASD young people: (1) AIS will be the main predictor of internalising problems; (2) ADB will be the main predictor of ADHD symptoms; (3) all ODD traits will independently predict conduct disorder symptoms; (4) vindictiveness will be the main predictor of aggressive conduct problems. Our findings using both parent and teacher data were consistent with the non-ASD ODD literature. AIS were associated with internalising but not externalising problems; ADB and vindictiveness were associated with externalising but not internalising problems; and vindictiveness was the main predictor of aggression. The DSM-5 tripartite model of ODD appears to be generalisable to ASD: for people with an autistic disorder, AIS, ADB and vindictive dimensions of oppositionality have distinct associations with concurrent psychopathology, suggesting the need to assess them as separate constructs.     

Pilgrims sailing the Titanic: Plausibility effects on memory for misinformation

People rely on information they read even when it is inaccurate (Marsh, Meade, & Roediger, Journal of Memory and Language 49:519–536, 2003), but how ubiquitous is this phenomenon? In two experiments, we investigated whether this tendency to encode and rely on inaccuracies from text might be influenced by the plausibility of misinformation. In Experiment 1, we presented stories containing inaccurate plausible statements (e.g., “The Pilgrims’ ship was the Godspeed”), inaccurate implausible statements (e.g., . . . the Titanic), or accurate statements (e.g., . . . the Mayflower). On a subsequent test of general knowledge, participants relied significantly less on implausible than on plausible inaccuracies from the texts but continued to rely on accurate information. In Experiment 2, we replicated these results with the addition of a think-aloud procedure to elicit information about readers’ noticing and evaluative processes for plausible and implausible misinformation. Participants indicated more skepticism and less acceptance of implausible than of plausible inaccuracies. In contrast, they often failed to notice, completely ignored, and at times even explicitly accepted the misinformation provided by plausible lures. These results offer insight into the conditions under which reliance on inaccurate information occurs and suggest potential mechanisms that may underlie reported misinformation effects.