Empowerment or Engagement? Digital Health Technologies for Mental Healthcare

We argue that while digital health technologies (e.g. artificial intelligence, smartphones, and virtual reality) present significant opportunities for improving the delivery of healthcare, key concepts that are used to evaluate and understand their impact can obscure significant ethical issues related to patient engagement and experience. Specifically, we focus on the concept of empowerment and ask whether it is adequate for addressing some significant ethical concerns that relate to digital health technologies for mental healthcare. We frame these concerns using five key ethical principles for AI ethics (i.e. autonomy, beneficence, non-maleficence, justice, and explicability), which have their roots in the bioethical literature, in order to critically evaluate the role that digital health technologies will have in the future of digital healthcare

An Investigation of the Influence of Hope on the Relationship between Racial Discrimination and Depressive Symptoms Among African American College Students

The relationship between perceived racial discrimination, hope, and depressive symptoms among African American college students was investigated. The first supported hypotheses were that racial discrimination, hope, and hope\u27s two components, agency and pathways, would each significantly affect depressive symptoms. Hope and pathways, but not agency, were each found to moderate the relationship between racial discrimination and depressive symptoms and the moderation models were found to explain as much or more variance the models examining direct effects. Also, the interaction of pathways and racial discrimination explained more variance than any of the other models. These results suggest that hope and pathways influence the relationship between racial discrimination and depressive symptoms. Implications for understanding how hope can influence the experience of discrimination for African American college students are discussed

On the Practical Consequences of Misfit in Mokken Scaling

Mokken scale analysis is a popular method to evaluate the psychometric quality of clinical and personality questionnaires and their individual items. Although many empirical papers report on the extent to which sets of items form Mokken scales, there is less attention for the effect of violations of commonly used rules of thumb. In this study, the authors investigated the practical consequences of retaining or removing items with psychometric properties that do not comply with these rules of thumb. Using simulated data, they concluded that items with low scalability had some influence on the reliability of test scores, person ordering and selection, and criterion-related validity estimates. Removing the misfitting items from the scale had, in general, a small effect on the outcomes. Although important outcome variables were fairly robust against scale violations in some conditions, authors conclude that researchers should not rely exclusively on algorithms allowing automatic selection of items. In particular, content validity must be taken into account to build sensible psychometric instruments

Social and Situational Influences on the Performance Rating Process

The effects of social and situational influences on the performance rating process has received relatively little attention by past research, yet merits increased attention. While there has been greater acknowledgment of the role of social and situational factors on rater cognition and evaluation, research has typically proceeded in a piecemeal fashion, isolating on a single influence at a time. This approach fails to recognize that performance rating is a process with multiple social and situational influences that need to be considered simultaneously. In the present study, a model of the performance rating process was tested, employing several social and situational variables that have been infrequently investigated and typically not in conjunction with one another. Results indicated support for the overall model and specific influences within the model. Implications of the results for performance rating research are discussed