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01-1.24) were independently associated with the risk of HE-associated symptoms.<br /><br /> Because of increased handwashing during the COVID-19 outbreak, there is a significant increase in HE-associated symptoms in HCWs. Proper education and preventive strategies for HE are urgently needed for HCWs fighting on the front lines of COVID-19.<br /><br />Because of increased handwashing during the COVID-19 outbreak, there is a significant increase in HE-associated symptoms in HCWs. Proper education and preventive strategies for HE are urgently needed for HCWs fighting on the front lines of COVID-19.Eckard and Lattal (2020) summarized the behavioristic view of hypothetical constructs and theories, and then, in a novel and timely manner, applied this view to a critique of internal clock models of temporal control. In our three-part commentary, we aim to contribute to the authors' discussion by first expanding upon their view of the positive contributions afforded by constructs and theories. We then refine and question their view of the perils of reifying constructs and assigning them causal properties. Finally, we suggest to behavior analysts four rules of conduct for dealing with mediational theories tolerate constructs proposed with sufficient reason; consider them seriously, both empirically and conceptually; develop alternative, behavior-analytic models with overlapping empirical domains; and contrast the various models. Through variation and selection, behavioral science will evolve.Eckard and Lattal's Perspectives on Behavior Science, 43(1), 5-19 (2020) critique of internal clock (IC) mechanisms is based on narrow concepts of clocks, of their internality, of their mechanistic nature, and of scientific explanations in general. This reply broadens these concepts to characterize all timekeeping objects-physical and otherwise-as clocks, all intrinsic properties of such objects as internal to them, and all simulatable explanations of such properties as mechanisms. Eckard and Lattal's critique reflects a restrictive billiard-ball view of causation, in which environmental manipulations and behavioral effects are connected by a single chain of contiguous events. In contrast, this reply offers a more inclusive stochastic view of causation, in which environmental manipulations are probabilistically connected to behavioral effects. From either view of causation, computational ICs are hypothetical and unobservable, but their heuristic value and parsimony can only be appreciated from a stochastic view of causation. Billiard-ball and stochastic views have contrasting implications for potential explanations of interval timing. As illustrated by accounts of the variability in start times in fixed-interval schedules of reinforcement, of the two views of causality examined, only the stochastic account supports falsifiable predictions beyond simple replications. It is thus not surprising that the experimental analysis of behavior has progressively adopted a stochastic view of causation, and that it has reaped its benefits. This reply invites experimental behavior analysts to continue on that trajectory.The motivating operations concept has improved the precision of our approach to analyzing behavior; it serves as a framework for classifying events that alter the reinforcing and punishing effectiveness of other events. Nevertheless, some aspects of the concept are seriously flawed, thereby limiting its utility. We contend in this article that the emphasis it places on the onset of some stimuli (putative motivating operations) making their offset a reinforcer in the absence of a learning history (i.e., in the case of unconditioned motivating operations), or because of such a history (i.e., in the case of reflexive conditioned motivating operations), is of no value in predicting or controlling behavior. It is unfortunate that this pseudo-analysis has been widely accepted, which has drawn attention away from actual motivating operations that are relevant to negative reinforcement, and led to conceptually flawed explanations of challenging human behaviors that are escape-maintained. When used appropriately, the motivating operations concept can help to clarify the conditions under which a stimulus change (in particular, stimulus termination) will function as a negative reinforcer. From both a theoretical and a practical perspective, rethinking the application of the motivating operations concept to negative reinforcement is advantageous. Herein, we explore the implications of doing so with the aim of encouraging relevant research and improving the practice of applied behavior analysis.Literature reviews allow professionals to identify effective interventions and assess developments in research and practice. As in other forms of scientific inquiry, the transparency of literature searches enhances the credibility of findings, particularly in regards to intervention research. The current review evaluated the characteristics of search methods employed in literature reviews appearing in publications concerning behavior analysis (n = 28) from 1997 to 2017. Specific aims included determining the frequency of narrative, systematic, and meta-analytic reviews over time; examining the publication of reviews in specific journals; and evaluating author reports of literature search and selection procedures. Narrative reviews (51.30%; n = 630) represented the majority of the total sample (n = 1,228), followed by systematic (31.51%; n = 387) and meta-analytic (17.18%; n = 211) reviews. In contrast to trends in related fields (e.g., special education), narrative reviews continued to represent a large portion of published reviews each year. The evaluated reviews exhibited multiple strengths; nonetheless, issues involving the reporting and execution of searches may limit the validity and replicability of literature reviews. A discussion of implications for research follows an overview of findings.Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. [https://www.selleckchem.com/products/Y-27632.html Y-27632 supplier] We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets.