Endicott College is a private college in Beverly, Massachusetts.
Previous researchers have asserted that interprofessional collaboration is an important component of behavior analytic practice; however, survey results have shown that the current state of interprofessional collaboration is varied. Bowman et al. (2024) surveyed 166 professionals from various disciplines to understand their perceptions and experiences collaborating with behavior analysts. Although many of these professionals valued collaboration with behavior analysts, and reported positive perceptions and experiences, a fair number shared critical descriptions of collaboration with behavior analysts. These perceptions often varied significantly by discipline suggesting that some interprofessional relationships face unique challenges. The purpose of the current study was to understand behavior analysts’ perceptions and experiences collaborating within specific interprofessional relationships. Additionally, through a self-reflection survey, we also sought to understand how behavior analysts viewed their own skills and behaviors in interprofessional collaboration. Overall, behavior analysts reported that many of their colleagues infrequently exhibit skills that are important to effective collaborative practice. On the self-reflection portion, the large majority of behavior analysts reported that they themselves engaged in collaborative behaviors frequently. These results do not align with the perceptions reported by Bowman et al. (2024), suggesting that changes in behaviors and perceptions may be needed to improve these collaborative relationships. Implications of these discrepancies are discussed as it relates to interprofessional collaboration involving behavior analysts.
Alternating treatment designs (ATD) allow practitioners to rapidly compare the effectiveness of multiple interventions without needing long baseline phases. ATDs are typically evaluated using visual analysis and effect size analysis. However, past research suggests that different raters may come to different conclusions when analyzing these graphs. Furthermore, agreement between raters does not equate to accuracy. Artificial Intelligence (AI) technologies may improve the replicability of decision-making while performing adequately with smaller datasets. Within AI, Machine Learning (ML) algorithms can learn patterns and make data-driven predictions. These algorithms can quantify complex, intuitive patterns directly from data that would be difficult or impractical to define explicitly with traditional programming rules. This study investigates the use of ML technology to analyze ATD graphs. Specifically, the researchers examined which feature engineering techniques resulted in predictive performance for an ML model trained on simulated or non-simulated data and tested on non-simulated data. The best-performing models achieved classification accuracy above 90
The central cholinergic system plays a crucial role in neural communication and physiological regulation, mediated by acetylcholine (ACh) and cholinergic receptors in the central nervous system (CNS). In this review, we explore the extensive distribution and impact of the central cholinergic system and its pivotal involvement in Parkinson's disease (PD). Despite PD being traditionally perceived as primarily a dopaminergic disorder, it exhibits significant cholinergic alterations, contributing to both motor and non-motor symptoms. These PD-specific alterations manifest as neuroanatomical changes, diminished acetylcholinesterase activity, and functional disturbances across various brain regions, impacting cognition, mood, sensory perception, sleep, and motor function. Comprehension of these cholinergic dysfunctions is paramount for the development of targeted therapies aimed at alleviating these PD symptoms. To provide further insight, we explore the therapeutic potential of nicotinic acetylcholine receptors (nAChRs) in PD, highlighting their role in preventing apoptosis, modulating neuroinflammation, and mitigating CNS damage. In summary, this review underscores the critical importance of cholinergic mechanisms in PD pathology and champions cholinergic-based interventions for enhanced patient outcomes and improved quality of life.
Caregivers' vocal imitation provided contingently on infant vocalization is an essential factor in early language development. While there is a large body of research using contingent vocal imitation (CVI) procedures with infants, there is limited research on applying the CVI procedure to young children with autism spectrum disorder (ASD). This study examined the effectiveness of the CVI procedure implemented by caregivers (staff members) to increase the vocalizations and echoic responses (imitative responses) of four children with ASD. The effects of CVI were compared to a noncontingent vocalization control condition using a modified alternating treatments design, with a baseline and reversal. All four participants demonstrated an increase in vocalizations in the CVI phase. Two participants demonstrated increased echoic (imitative) responses in the CVI conditions compared to the initial baseline condition and control condition. We discuss the implications of these findings for application and future research with younger children with ASD and in multiple settings and caregivers.
ABSTRACT Decision models are frequently published in applied behavior analysis (ABA) to guide clinical decision‐making, yet few have been empirically tested for their validity. To help researchers and practitioners identify potential weaknesses in decision models, the present study demonstrates one analytic method researchers can use to assess the procedural validity of decision models. Participants ( N = 75) were presented with systematically designed hypothetical vignettes to comprehensively test all decision pathways. Each vignette described a client, challenging behavior, and the targeted therapeutic environment (e.g., school, home) similar to what might be collected during an intake process. Participants were then asked to select the best data collection procedure for their initial behavioral observation either without (baseline) or with access to an associated decision model. Access to the decision model did not significantly improve performance for the control or behavior analyst group. Once these data were collected, we demonstrate how an error analysis allows researchers to identify which decision points incorrect decisions cluster at, providing information on how to improve the language of the decision model. Having an analytic technique that identifies challenges in decision model language and decision paths will aid future researchers in the iterative process i.e. decision model design and validation.
