INTRODUCTION

Teleworking is one of the most significant legacies of the pandemic. Great attention is now being paid to its effects on workers' health. One of the arguments that emerged on this issue is that 'working away from the office' affects the time we spend with significant others. This calls into question all those processes that make relatives and colleagues important to our health, such as forms of mentoring and social support, but also conflicts, work interruptions or control over workers' activities. So far, no study has evaluated the impact that teleworking has on these processes using data on personal networks. is the first study to use social network analysis to measure the impact that telework has on social relations and, in turn, workers' health and well-being.

METHODS AND ANALYSIS

The project draws on a total sample of 4400 participants from Switzerland, the Netherlands, Spain and Germany (n=1100 per country). The choice of these countries is due to their specificity and diversity in socioeconomic features, which make them particularly interesting for studying teleworking from a comparative point of view. The research is conceived as a sequential mixed-method design. First, quantitative data collection will administer an online questionnaire to gather information on telework modalities, health and well-being markers, and data on personal networks collected by a name generator. A qualitative module, administered one year later, will consist of in-depth interviews with a subsample (n=32) of teleworkers selected for delving narratively into the mechanisms identified with the quantitative analyses.

ETHICS AND DISSEMINATION

This study has obtained 2 years of funding from the Swiss Network for International Study and was approved by the Ethics Committee of the University of Geneva (CUREG-20230920-292-2). All participants will be asked to provide informed consent to participate in this study. The results will be shared with international organisations and disseminated in scientific journals and conferences. Fully anonymised data will be made available on the Open Science Framework (OSF) data-repository platform.