Burnout among behavior analysts in times of crisis: The roles of work demands, professional social support, and psychological flexibility

Background The purpose of this study was to apply the Job-Demands Resources model as a framework to examine the relationship between work demands and burnout among behavior analysts working in human service settings during the Covid-19 pandemic. We specifically investigated the work demands-burnout relationship and how psychological flexibility, supervisor support, and coworker support related to burnout, whether as moderators or as mediators of work demands. Method A sample of 298 ABA practitioners participated in a cross-sectional investigation in April-May 2021 and reported sociodemographic and job-related characteristics, information about the impact of Covid-19 on work, work demands, psychological flexibility, supervisor support, and coworker support. We conducted regression-based analyses of moderation and multiple mediation. Results Greater work demands predicted higher levels of exhaustion and disengagement. Psychological flexibility and supervisor support partially mediated the influence of work demands on exhaustion and burnout;neither moderated the influence of work demands on exhaustion or burnout. Coworker support did not function as a moderator or mediator in the work demands-burnout relationship. Conclusions Findings highlight the need for effective caseload management and supervision practices, along with initiatives that create opportunities for increased, positive interaction with supervisors and among colleagues. Providing psychological flexibility skills training may benefit behavior analysts as a burnout management strategy. As a personal resource, however, it may be insufficient to mitigate burnout in times of crisis when work demands are higher than usual. Organizations should consider multi-faceted burnout interventions that address personal and workplace factors to both increase personal/job resources and reduce job demands.

What is the role of psychologists during the COVID-19 Pandemic? The necessity of taking actions reflecting public voices

This paper discusses what psychologists should do to help the general public in response to the new coronavirus infectious disease (COVID-19) in Japan. Four things psychologists could do are listed in the Japanese Psychological Association article entitled "Against Violence in the Home." However, the style of the article seems insufficient for psychologists to perceive the article as general behavioral guidelines for helping people suffering from psycho logical stress. Psychologists should be encouraged to publicly disseminate relevant information because the current domestic policies may cause confusion for the public as they provide insufficient behavioral principles and re- search-based perspectives. This paper proposes three different levels of roles for taking actions as psychologists: as a professional individual, as a member of an academic society, and as a public figure disseminating relevant in formation. To be impactful in carrying out these roles, setting up a "place" where individual practices and opinions can be quickly aggregated is required. Moreover, it is important for psychologists to listen to public voices and be prepared to focus their professional Eves on tackling social issues. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Impacts of COVID-19 on Future Preferences Toward Telework

This paper presents a study in capturing the impacts of the mandatory pandemic-induced telework practice on workers' perceptions of the benefits, challenges, and difficulties associated with telecommuting and how those might influence their preference for telework in the future. Data was collected through an online survey conducted in South Florida in May 2020. Survey data showed that telework indices (either measured through actual behavior or stated preference) before, during, and after the pandemic were heterogeneous across socio-economic, demographic, and attitudinal segments. Before the outbreak, males, full-time students, those with PhD degrees, and high-income people showed higher percentages of involvement in jobs with a telework option. They also had higher pro-technology, pro-online education, workaholic, and pro-telework attitudes. During the pandemic, professional/managerial/technical jobs as well as jobs with lower physical-proximity measures showed the highest telework frequency. In view of future telework preferences, our analysis showed that those who were more pro-telework, pro-technology, and showed less dislike of telework dislike preferred higher telework frequency. A structural equation model was developed to assess the impacts of different predictors on telework behavior before the pandemic and preferences after the pandemic. While telework frequency before the pandemic was highly affected by the pro-telework attitude, the after-pandemic preferences were influenced by several other attitudes such as dislike telework, enjoy interaction, workaholic, as well as productivity factors. This might confirm the assumption that the mandatory practice through the pandemic has provided employees more experiences with work-from-home arrangements, which could reshape decisions and expectations around telework adoption in the future. © National Academy of Sciences: Transportation Research Board 2021.

Effects of COVID-19 on Telework and Commuting Behavior: Evidence from 3 Years of Panel Data.

The COVID-19 pandemic has forced employers and employees to re-evaluate their attitudes toward telecommuting. This induced a change in the sheer number of people who have started to work from home (WFH). While previous studies highlight differences between telecommuters based on their level of telecommuting experience, these effects have not been studied in detail. This may limit the evaluation of implications for post-pandemic times and the transferability of models and predictions based on data collected during the COVID-19 pandemic. This study expands on previous findings by comparing the characteristics and behavior of those who have started to telecommute during the pandemic and those who had already telecommuted before. Furthermore, this study addresses the uncertainty that exists about whether the findings of studies conducted before the pandemic-for example about sociodemographic characteristics of telecommuters-still hold true, or if the pandemic induced a shift in telecommuters' profiles. Telecommuters show differences when considering their previous experience in WFH. The results of this study suggest that the transition induced by the pandemic was more drastic for new telecommuters compared with experienced telecommuters. The COVID-19 pandemic had an effect on how household configurations are considered in the choice to WFH. With decreased access to child care resulting from school closings, people with children in the household were more likely to choose to telecommute during the pandemic. Also, while people living alone are generally less likely to choose to WFH, this effect was reduced as a result of the pandemic.

Longitudinal Analysis of COVID-19 Impacts on Mobility: An Early Snapshot of the Emerging Changes in Travel Behavior.

The COVID-19 pandemic has caused a huge disruption worldwide with direct and indirect effects on travel behavior. In response to extensive community spread and potential risk of infection, during the early stage of the pandemic many state and local governments implemented non-pharmaceutical interventions that restricted non-essential travel for residents. This study evaluates the impacts of the pandemic on mobility by analyzing micro panel data (N = 1,274) collected in the United States via online surveys in two periods, before and during the early phase of the pandemic. The panel makes it possible to observe initial trends in travel behavior change, adoption of online shopping, active travel, and use of shared mobility services. This analysis intends to document a high-level overview of the initial impacts to spur future research to dive deeper into these topics. With the analysis of the panel data, substantial shifts are found from physical commutes to teleworking, more adoption of e-shopping and home delivery services, more frequent trips by walking and biking for leisure purposes, and changes in ridehailing use with substantial variations across socioeconomic groups. The social and environmental implications of these findings are discussed and suggestions for effective policy and directions for future research are made in the conclusion.

COVID-19: Modeling Out-of-Home and In-Home Activity Participation during the Pandemic.

Understanding the interaction between in-home and out-of-home activity participation decisions is important, particularly at a time when opportunities for out-of-home activities such as shopping, entertainment, and so forth are limited because of the COVID-19 pandemic. The travel restrictions imposed as a result of the pandemic have had a massive impact on out-of-home activities and have changed in-home activities as well. This study investigates in-home and out-of-home activity participation during the COVID-19 pandemic. Data comes from the COVID-19 Survey for assessing Travel impact (COST), conducted from March to May in 2020. This study uses data for the Okanagan region of British Columbia, Canada to develop the following two models: a random parameter multinomial logit (RPMNL) model for out-of-home activity participation and a hazard-based random parameter duration (HRPD) model for in-home activity participation. The model results suggest that significant interactions exist between out-of-home and in-home activities. For example, a higher frequency of out-of-home work-related travel is more likely to result in a shorter duration of in-home work activities. Similarly, a longer duration of in-home leisure activities might yield a lower likelihood for recreational travel. Health care workers are more likely to engage in work-related travel and less likely to participate in personal and household maintenance activities at home. The model confirms heterogeneity among the individuals. For instance, a shorter duration of in-home online shopping yields a higher probability for participation in out-of-home shopping activity. This variable shows significant heterogeneity with a large standard deviation, which reveals that sizable variation exists for this variable.

Modelling the Long-Term Expected Impact of the Covid-19 Crisis on Commute and Telecommute

The Covid-19 pandemic crisis has forced many people to work from home rather than at their regular workplace. This paper explores the expected long-term changes caused by the pandemic crisis in Israel on work-related travel patterns, that is, the shifts in commuting and telecommuting post- versus pre-pandemic. Methodologically, the analysis is based on two consecutive surveys (of the same respondents) that were distributed during the pandemic (April and June 2020) to evaluate the trends in commuting and telecommuting from pre- to post-pandemic, addressing revealed preferences on work habits before and during the pandemic and stated intentions about work patterns after the pandemic. Four models were estimated based on these data: two multinomial logit models analyzing the trends in commuting and teleworking from before to after the pandemic, and two ordered logit models addressing the frequency of the intended teleworking and commuting trips in the post-pandemic era. The results showed that the Covid-19 crisis is expected to have some long-term implications, specifically, based on our sample, a 5%-6% expected reduction in commuting trips, alongside an expected increase in teleworking. While several socio-demographic, work-related, and personality traits were found to significantly influence commuting/telecommuting trends and frequency, it is interesting to note that working solely from home during the lockdown was found to have a prominent impact on increasing teleworking while decreasing commuting. Quantitative consistency evaluation of behavioral-shift statements across the consecutive surveys revealed moderate consistency, which is very reasonable given the instability associated with the Covid-19 crisis and the inherent changes in human perceptions.

Assessing the social validity of telehealth-based applied behavior analysis services for autism spectrum disorder

During the COVID-19 pandemic, applied behavior analysis services for many autistic individuals were transitioned to telehealth. The current study assessed caregiver-reported quality of life (QoL) and social validity for families of autistic children receiving only telehealth services (n = 96) or a combination of telehealth and in-person services (n = 173). Barriers to the telehealth experience were analyzed via an ANOVA, and the impact of funding source was analyzed using an independent samples t-test. Caregivers reported benefit across QoL and social validity items, with scores ranging from 3.31 to 4.44 (1 = least benefit, 5 = most benefit). While many caregivers reported no barriers regarding technology (44.61%), childcare (69.52%), and employment (64.68%), the presence of those barriers significantly impacted QoL and social validity scores. Funding source was not found to have a significant impact. Overall, caregivers found value in their child's telehealth services. Clinicians have an obligation to mitigate barriers to ensure the success of the intervention. © 2023 John Wiley & Sons Ltd.

Hybrid analysis of the learner's online behavior based on learning style

Since the covid pandemic, universities propose online education to ensure learning continuity. However, the insufficient preparation led to a major drop in the learner's performance and his/her dissatisfaction with the learning experience. This may be due to several reasons, including the insensitivity of the virtual learning environment to the learner's preferences. We propose to address the issue of student's dissatisfaction and lack of interaction, by integrating learning style theory into the analysis of the learner's online behavior. Our work differentiates itself from the rest of researches that employed learning style theory by its two step process. First, we classify the learning activities into learning categories based on learning style theory. Second, we define behavioral features that quantify the learner's behavior across the learning categories. The analysis of the learner's online behavior based on the behavioral features revealed new aspects of the learner's preferences. We consider these findings to be best useful for developing learning style-sensitive adaptive learning environments. Nevertheless, the behavioral features could be beneficial in different contexts. In fact, when applied to course outcome prediction, the behavioral features enhanced the results by 10%. The latter indicates that behavioral features reflected the correlation between behavior and academic performance. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

SA-FGDEM: A Self-adaptive E-Learning Performance Prediction Model

With the rapid development of computer computing power and the severe challenges brought by the COVID-19, e-learning, as the optimal solution for most students and other learner groups, plays an extremely important role in maintaining the normal operation of educational institutions. As the user community continues to expand, it has become increasingly important to guarantee the quality of teaching and learning. One way to ensure the quality of online education is to construct e-learning behavior data to build learning performance predictors. Still, most studies have ignored the intrinsic correlation between e-learning behaviors. Therefore, this study proposes an adaptive feature fusion-based e-learning performance prediction model (SA-FGDEM) relying on the theoretical model of learning behav-ior classification. The experimental results show that the feature space mined by fine-grained differential evolution algorithm and the adaptive feature fusion combined with differential evolution algorithm can support e-learning performance prediction more effectively and is better than the benchmark method. © 2022 IEEE.

Machine Learning Approach: Consumer Buying Behavior Analysis

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

Improving student academic productivity within remote instruction: Investigating the effectiveness of virtual CICO

School Wide Positive Behavior Intervention and Supports is a framework by which schools can assess and develop a system of preventative strategies to decrease the likelihood of problematic behaviors. SWPBIS provides for a three tiered system of interventions with increasing supports and complexity to meet students' needs at each level. CICO was developed as a Tier 2 procedure to be implemented for students that are not demonstrating responsiveness to Tier 1 interventions within a well-developed SWPBIS framework and is intended to be implemented with students that are exhibiting disruptive non-dangerous problematic behaviors such as calling out, out of seat behavior, low productivity, and non-compliance. The research which supports the potential flexibility of CICO intervention's applicability across a wide range of modifications empowers school administrators, teachers, and behavior specialists with the confidence to apply variations of these procedures to atypical circumstances that may arise. In March of 2020, the COVID-19 global pandemic prompts schools across America to implement a novel instructional approach which allows students to attend school remotely through online access. The move towards online schooling requires a re-imagining of how behavioral support can be delivered through virtual instruction. This study reviewed the application and effectiveness of the use of Check In/ Check Out (CICO) implemented virtually during remote instruction through a commonly shared computer based platform (i.e. Google Classroom). (PsycInfo Database Record (c) 2023 APA, all rights reserved)

SunFA - An open-source application for behavior analysis in online video-conferencing

Video-conferencing applications are becoming increasingly popular, especially with the remote working trend after COVID-19. The benefits of meeting online cannot be denied;however, this is still quite limited. In particular, it is essential to monitor and analyze participants' behavior. In this paper, we proposed SunFA - an open-source participants analysis tool for video-conferencing based on face analysis and virtual camera technology. The advantage of our system is that it is compatible with almost available video conferencing applications, such as Google Meet, Skype, Microsoft Teams, Zoom, Slack, etc. Furthermore, we packaged this software as a desktop application for Windows operating system to make it easy to install. The memory usage and execution time evaluation ensure the real-time and resource-saving of a video-conferencing application. We open-source our entire source code and solutions at https://github.com/sun-asterisk-research/sun-fa © 2022 IEEE.

A Dynamic Access Control Model Based on Attributes and Intro VAE

Affected by the COVID-19 pandemic, teleworking is becoming more popular, with the exposed attack surface of the internal network expanding. Once outsiders personate accounts or insiders conduct illegal operations, the data security in teleworking with traditional border protection will be broken. Therefore, it is necessary to implement fine-grained and dynamic access control to protect data from malicious access. Attribute-based access control (ABAC) is ideal, where authorization is performed through attributes and rules. On this basis, risk assessment, context awareness, and machine learning are supplemented for dynamic access control. However, these methods have their limitations due to the requirement of sufficient prior knowledge and massive label-classified data. Moreover, it is challenging to obtain the samples of attack behaviors, and the attack behaviors may change frequently to evade detection. In contrast, the normal behaviors are relatively stable except for the update of network services. We propose a dynamic access control model, ABAC-IntroVAE, to address the above issues. ABAC-IntroVAE judges users' requests through rule matching and behavior analysis based on the attributes of the requests. It first filters out requests against the rules by rule matching. Then, the introspective variational autoencoder (IntroVAE) is used for behavior analysis to realize dynamic access decisions. Requests classified as normal can be authorized for access. ABAC-IntroVAE only needs samples of normal requests for training, avoiding the difficult task of collecting massive and frequently changing samples of attack requests. Meanwhile, the IntroVAE model is updated through continual learning to adapt to new-style normal behaviors due to the update of network services. Our experiment study suggests that our proposed ABAC-IntroVAE can effectively perform dynamic access control. It achieves an accuracy of 97.2% in abnormal detection and maintains an accuracy of over 97% through continual learning, despite the addition of new-style user behavior patterns. © 2022 IEEE.

A Descriptive Review of Telehealth for Individuals With Autism Spectrum Disorder.

Since the early 2000s, telehealth has been used to provide behavior analytic intervention to individuals with autism spectrum disorder (ASD). Evaluating evidence supporting telehealth remains valuable, especially as there has been increased accessibility since the COVID-19 pandemic. Although there is empirical support for telehealth as an effective service-delivery option, important variables (e.g., costs, implementer training) remain unknown. Despite potential roles in telehealth service-delivery models, a careful review of participant prerequisite skills, implementer characteristics (e.g., experience, education), technology variables (e.g., HIPAA compliance), and skill(s) targeted (i.e., mastered or untrained skills) have not been considered. Therefore, we aimed to extend prior telehealth literature reviews by evaluating current research across variables important for telehealth service-delivery involving individuals with ASD. We found thorough descriptions of participants and implementers, implementer training, and social validity evaluations. Limitations of telehealth literature include exclusion of teen and adult participants, limited description of prerequisite skills and evaluations of direct telehealth interventions. Future research areas were identified.

Natural Language Processing Techniques for Behavior Analysis in Social Networks of Hispanic American University Communities

The Covid-19 confinement has forced certain human groups to look for alternatives to socialize. University communities did not stay out of this context. The presence of student communities called 'confessions' whose purpose is to anonymously express their problems, desires and interests stands out. The main objective of this research is to determine the topics that highlight the cultural aspects and interests of these communities. Confessions pages from 5 Spanish-speaking countries were analyzed. Experiments were carried out on Facebookand Instagram posts using word embeddings and KMeans to cluster the semantic vector space. Anew evaluation approach based on the state-of-the-art was proposed that allow us to select and analyze topic models through specific keywords. As a result, topics of general interest were identified for each community such as 'Academic life', 'Relationships', 'Politics' and 'Personal problems'. The results vary by country. The collected dataset is publicly available1 for any academic purposes. © 2022 IEEE.

Behaviour Analysis of Izmir Residents Using Public Wi-Fi Access Point Usage

The wide distribution of access points in Izmir allows the collected information to be employed in smart city algorithms. In this study, we analyze the information that has been made publicly available by Izmir Metropolitan Municipality. We first show that the data is reliable, then analyze it from the perspectives of holidays, seasonal trends, and the COVID-19 pandemic. The study also shows that the information can be used for crowd analysis and forecasting, using K-means and SARIMA algorithms, respectively. © 2022 IEEE.

The Covid-19 Influence on the Desire to Stay at Home: A Big Data Architecture

The COVID-19 pandemic has had an impact on many aspects of society in recent years. The ever-increasing number of daily cases and deaths makes people apprehensive about leaving their homes without a mask or going to crowded places for fear of becoming infected, especially when vaccination was not available. People were expected to respect confinement rules and have their public events cancelled as more restrictions were imposed. As a result of the pandemic's insecurity and instability, people became more at ease at home, increasing their desire to stay at home. The present research focuses on studying the impact of the COVID-19 pandemic on the desire to stay at home and which metrics have a greater influence on this topic, using Big Data tools. It was possible to understand how the number of new cases and deaths influenced the desire to stay at home, as well as how the increase in vaccinations influenced it. Moreover, investigated how gatherings and confinement restrictions affected people's desire to stay at home. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Improving student academic productivity within remote instruction: Investigating the effectiveness of virtual CICO

School Wide Positive Behavior Intervention and Supports is a framework by which schools can assess and develop a system of preventative strategies to decrease the likelihood of problematic behaviors. SWPBIS provides for a three tiered system of interventions with increasing supports and complexity to meet students' needs at each level. CICO was developed as a Tier 2 procedure to be implemented for students that are not demonstrating responsiveness to Tier 1 interventions within a well-developed SWPBIS framework and is intended to be implemented with students that are exhibiting disruptive non-dangerous problematic behaviors such as calling out, out of seat behavior, low productivity, and non-compliance. The research which supports the potential flexibility of CICO intervention's applicability across a wide range of modifications empowers school administrators, teachers, and behavior specialists with the confidence to apply variations of these procedures to atypical circumstances that may arise. In March of 2020, the COVID-19 global pandemic prompts schools across America to implement a novel instructional approach which allows students to attend school remotely through online access. The move towards online schooling requires a re-imagining of how behavioral support can be delivered through virtual instruction. This study reviewed the application and effectiveness of the use of Check In/ Check Out (CICO) implemented virtually during remote instruction through a commonly shared computer based platform (i.e. Google Classroom). (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Religious Discourse Analysis regarding Anxiety

In the midst of COVID-19 pandemic, in the city of Fortaleza (Ceara State - Brazil), the religious pursuit for anxiety treatment disorders was noticed. From then on, this article arose, whose objective was to analyze the implications of the religious discourse of two churches in the state for anxious responses, according to the Behavior Analysis. In order to achieve this, a bibliographic review was conducted, followed by the selection, transcription and content analysis of the videos on the topic, over the religious perspective, of the "Reformed Church " and the "Protestant Community Church ", chosen randomly. Therefore, it became evident that the "Reformed Church " proved to have less negative implications for illness than the "Protestant Community Church ", although there was no referral to Mental Health professionals by both institutions.