Working conditions during the COVID-19 pandemic in primary and tertiary healthcare: a comparative cross-sectional study.

Objectives: The COVID-19 pandemic has globally affected healthcare workers' (HCWs) health and wellbeing. Most studies on COVID-19 have focused on tertiary healthcare. The aim of this study was to increase the knowledge on the effects of the pandemic on working conditions in tertiary and primary healthcare.

" Healthcare workers' perspectives on infection prevention and control during the COVID-19 pandemic.

Objectives: The COVID-19 pandemic has posed several risk factors to healthcare workers' (HCWs') emotional distress. The purpose of the study was to enhance understanding of the experiences and feelings of HCWs during the COVID-19 pandemic, with specific reference to infection prevention and control (IPC) practices and guidance, focusing on the quality and availability of personal protective equipment (PPE), guidelines, and management. With a qualitative approach, we aimed to enable a wider narrative; to gain a more detailed understanding related to PPE use and identify experiences that can be overlooked in forced-choice questionnaires.

Aerosol concentrations and size distributions during clinical dental procedures.

Background: Suspected aerosol-generating dental instruments may cause risks for operators by transmitting pathogens, such as the SARS-CoV-2 virus. The aim of our study was to measure aerosol generation in various dental procedures in clinical settings.

Methods: The study population comprised of 84 patients who underwent 253 different dental procedures measured with Optical Particle Sizer in a dental office setting.

Blood and saliva SARS-CoV-2 antibody levels in self-collected dried spot samples.

We examined the usefulness of dried spot blood and saliva samples in SARS-CoV-2 antibody analyses. We analyzed 1231 self-collected dried spot blood and saliva samples from healthcare workers. Participants filled in a questionnaire on their COVID-19 exposures, infections, and vaccinations.

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic.

During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed.