Experiences of face mask use during the COVID-19 pandemic: A qualitative study.

The use of face masks and coverings has been a central component of efforts to mitigate the impact of the COVID-19 pandemic and has been legally mandated in some countries. Most academic studies to date, however, have focussed primarily on its effectiveness in reducing SARS-CoV-2 transmission, largely neglecting the social dimensions of mask mandates. In this narrative interview-based study, we consider experiences of face masks, with a particular focus on groups considered to be at a potential disadvantage from compulsory masking. Drawing on 40 telephone, video-call and e-mail interviews, we highlight the impact of inconsistent communication and the notion of mask wearing as an act of altruism on participants' experiences. In particular, we show how intolerance towards individuals who did not wear masks could result in stigma and exclusion, regardless of the legitimacy of their reasons. We suggest that more is needed to mitigate the 'dark side' of discourses of collective effort and altruism at a time of societal stress and fracture, and to account for the needs and interests of groups for whom compulsory masking may result in further marginalisation.

Biometeorological effects on worker absenteeism.

The effects of six biometeorological variables (temperature, precipitation, air pressure, humidity, wind speed, and snow) on plant-wide worker absenteeism rates were investigated using 4 years of daily absence data (n=889). After holding constant temporal variables (years, season, and day of week), and then other biometeorological variables, all but one of the variables under consideration were uniquely and significantly related to absenteeism: temperature (r(partial)=-0.17***), precipitation (r(partial)=0.12***), air pressure (r(partial)=-0.09**), wind speed (r(partial)=0.11*), and snow (r(partial)=0.30***). Humidity (r(partial)=-00, ns) was not uniquely correlated. The adjusted R(2) of .29 (full R=0.55) for the entire model was also significant, illustrating the importance of these exogenous, meteorological variables in developing a prediction model of plant-wide absenteeism.