The Beck Depression Inventory-II (BDI-II) is currently one of the most widely used measures in both research and clinical practice for assessing depression. Although the psychometric properties of the scale have been well established through many studies worldwide, so far there is no study examining the validity and reliability of BDI-II in Republic Dominican. The purpose of the present study was twofold: (a) to examine the latent structure of BDI-II by testing several competing models proposed in the literature; and (b) to provide evidence of validity and reliability of the BDI-II in Republic Dominican. Confirmatory factor analysis indicated that a bifactor model with a general depression factor and three specific factors consisting of cognitive, affective and somatic showed the best fit to the data. Internal reliability was moderate to high for all subscales and for the total scale. Scores on BDI-II discriminated between clinical and general population, supporting for external validity. Practical implications are discussed and suggestions for further research are also made.
Growth mixture modeling is generally used for two purposes: (1) to identify mixtures of normal subgroups and (2) to approximate oddly shaped distributions by a mixture of normal components. Often in applied research this methodology is applied to both of these situations indistinctly: using the same fit statistics and likelihood ratio tests. This can lead to the overextraction of latent classes and the attribution of substantive meaning to these spurious classes. The goals of this study are (1) to explore the performance of the Bayesian information criterion, sample-adjusted BIC, and bootstrap likelihood ratio test in growth mixture modeling analysis with nonnormal distributed outcome variables and (2) to examine the effects of nonnormal time invariant covariates in the estimation of the number of latent classes when outcome variables are normally distributed. For both of these goals, we will include nonnormal conditions not considered previously in the literature. Two simulation studies were conducted. Results show that spurious classes may be selected and optimal solutions obtained in the data analysis when the population departs from normality even when the nonnormality is only present in time invariant covariates.
Background/Introduction
Psychological and physical well-being of health personnel has been significantly affected by COVID-19. Work overload and continuous exposure to positive COVID-19 cases have caused them fatigue, stress, anxiety, insomnia and other detriments. This research aims: 1) to analyze whether the use of cognitive reevaluation and emotional suppression strategies decreases and increases, respectively, stress levels of health personnel; 2) to quantify the impact of contact with patients with COVID-19 on stress levels of medical staff.
Method
Emotion regulation strategies (cognitive reevaluation and emotional expression) and stress levels were evaluated in 155 Dominican physicians who were treating people infected with COVID-19 at the moment of the study (67.9% women and 32.1% men; mean age = 34.89; SD = 9.26). In addition, a questionnaire created by the researchers quantified the impact that contact with those infected had on their stress levels.
Results
Contact with patients with COVID-19 predicts increased use of emotion suppression strategies, although is not associated with the use of cognitive reevaluation. These findings lead to an even greater increase in stress on health care providers.
Conclusions
Contextual contingencies demand immediate responses and may not allow health personnel to use cognitive re-evaluation strategies, leaning more towards emotion suppression. However, findings regarding high levels of stress require the implementation of intervention programs focused on the promotion of more functional emotion regulation strategies. Such programs may reduce current stress and prevent post-traumatic symptoms.
Although virtual reality (VR) usage has become widespread in the last decade, its adoption has been hampered by experiences of user discomfort known as cybersickness. The present study, in line with the “2020 cybersickness R&D agenda”, sought to provide a broad examination of the cybersickness phenomenon, assessing its pervasiveness, latent trajectories, impacts on the VR experience, and predictor variables. The study was composed of 92 participants living in the Dominican Republic with ages ranging from 18 to 52 years (
M
= 26.22), who experienced a 10-min VR immersion in two environments designed for psychotherapy. The results indicated that cybersickness was pervasive, with 65.2% of the participants experiencing it, and 23.9% severely. Additionally, the latent trajectories of cybersickness were positive and curvilinear, with large heterogeneity across individuals. Cybersickness also had a substantive negative impact on the user experience and the intentions to adopt the VR technology. Finally, motion sickness susceptibility, cognitive stress, and recent headaches uniquely predicted greater severity of cybersickness, while age was negatively related. These combined results highlight the critical role that cybersickness plays on the VR experience and underscore the importance of finding solutions to the problems, such as technological advancements or special usage protocols for the more susceptible individuals.
Supplementary Information
The online version contains supplementary material available at 10.1007/s10055-022-00636-4.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.