Reducing Test Anxiety by Device-Guided Breathing: A Pilot Study.

Test anxiety remains a challenge for students and has considerable physiological and psychological impacts. The routine practice of slow, Device-Guided Breathing (DGB) is a major component of behavioral treatments for anxiety conditions. This paper addresses the effectiveness of using DGB as a self-treatment clinical tool for test anxiety reduction. This pilot study sample included 21 healthy men and women, all college students, between the ages of 20 and 30. Participants were randomly assigned to two groups: DGB practice (n = 10) and wait-list control (n = 11). At the beginning and the end of 3-weeks DGB training, participants underwent a stress test, followed by measures of blood pressure and reported anxiety. Anxiety reduction in the DGB group as compared to controls was not statistically significant, but showed a large effect size. Accordingly, the clinical outcomes suggested that daily practice of DGB may lead to reduced anxiety. We assume that such reduction may lead to improved test performance. Our results suggest an alternative treatment for test anxiety that may also be relevant for general anxiety, which is likely to increase due to the ongoing COVID-19 pandemic.

Artificial intelligence (AI) acceptance in primary care during the coronavirus pandemic: What is the role of patients' gender, age and health awareness? A two-phase pilot study.

Background: Artificial intelligence (AI) is steadily entering and transforming the health care and Primary Care (PC) domains. AI-based applications assist physicians in disease detection, medical advice, triage, clinical decision-making, diagnostics and digital public health. Recent literature has explored physicians' perspectives on the potential impact of digital public health on key tasks in PC. However, limited attention has been given to patients' perspectives of AI acceptance in PC, specifically during the coronavirus pandemic. Addressing this research gap, we administered a pilot study to investigate criteria for patients' readiness to use AI-based PC applications by analyzing key factors affecting the adoption of digital public health technology. Methods: The pilot study utilized a two-phase mixed methods approach. First, we conducted a qualitative study with 18 semi-structured interviews. Second, based on the Technology Readiness and Acceptance Model (TRAM), we conducted an online survey (n = 447). Results: The results indicate that respondents who scored high on innovativeness had a higher level of readiness to use AI-based technology in PC during the coronavirus pandemic. Surprisingly, patients' health awareness and sociodemographic factors, such as age, gender and education, were not significant predictors of AI-based technology acceptance in PC. Conclusions: This paper makes two major contributions. First, we highlight key social and behavioral determinants of acceptance of AI-enabled health care and PC applications. Second, we propose that to increase the usability of digital public health tools and accelerate patients' AI adoption, in complex digital public health care ecosystems, we call for implementing adaptive, population-specific promotions of AI technologies and applications.

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming.

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.