Individual Characteristics in the Comprehension of Pandemic Video Communication: Randomized Controlled Between-Subjects Design.

Background: Video played an important role in health communication throughout the COVID-19 pandemic. It was used to communicate pandemic information to the public, with a variety of formats, presenters, and topics. Evidence regarding the effectiveness of video features is available, while how individual characteristics of recipients influence communication comprehension is still limited.

ComBat models for harmonization of resting-state EEG features in multisite studies.

Objective: Pooling multisite resting-state electroencephalography (rsEEG) datasets may introduce bias due to batch effects (i.e., cross-site differences in the rsEEG related to scanner/sample characteristics).

Exploring Norwegian homecare healthcare professionals' perceptions of risk and the link to high-quality care: a qualitative multiple case study.

Objectives: Homecare is a critical component of the ongoing restructuring of healthcare worldwide, given the shift from institution- to home-based care. The homecare evidence base still contains significant gaps: There is a lack of knowledge regarding quality and safety work and interventions. This study explores how home healthcare professionals perceive and use the concept of risk to guide them in providing high-quality healthcare while maintaining resilience.

Measuring digital stress in Norway: translation and validation of the Digital Stressors Scale.

Introduction: A better understanding of the effects of the widespread use of information and communication technology (ICT) among employees is important for maintaining their wellbeing, work-life balance, health, and productivity. Thus, having robust and reliable measurement instruments is crucial for quantifying the effects of ICT use, and facilitating the development of effective strategies to promote employee wellbeing.

Methods: Therefore, we translated the Digital Stressors Scale (DSS) to Norwegian and administered it to a convenience sample of 1,228 employees, using the forward-backward translation method.

Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study.

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability.