The four weeks before lockdown during the COVID-19 pandemic in Germany: a weekly serial cross-sectional survey on risk perceptions, knowledge, public trust and behaviour, 3 to 25 March 2020.

BackgroundDuring the COVID-19 pandemic, public perceptions and behaviours have had to adapt rapidly to new risk scenarios and radical behavioural restrictions.AimTo identify major drivers of acceptance of protective behaviours during the 4-week transition from virtually no COVID-19 cases to the nationwide lockdown in Germany (3-25 March 2020).MethodsA serial cross-sectional online survey was administered weekly to ca 1,000 unique individuals for four data collection rounds in March 2020 using non-probability quota samples, representative of the German adult population between 18 and 74 years in terms of age × sex and federal state (n = 3,910). Acceptance of restrictions was regressed on sociodemographic variables, time and psychological variables, e.g. trust, risk perceptions, self-efficacy. Extraction of homogenous clusters was based on knowledge and behaviour.ResultsAcceptance of restrictive policies increased with participants' age and employment in the healthcare sector; cognitive and particularly affective risk perceptions were further significant predictors. Acceptance increased over time, as trust in institutions became more relevant and trust in media became less relevant. The cluster analysis further indicated that having a higher education increased the gap between knowledge and behaviour. Trust in institutions was related to conversion of knowledge into action.ConclusionIdentifying relevant principles that increase acceptance will remain crucial to the development of strategies that help adjust behaviour to control the pandemic, possibly for years to come. Based on our findings, we provide operational recommendations for health authorities regarding data collection, health communication and outreach.

Frequency distribution of journalistic attention for scientific studies and scientific sources: An input-output analysis.

Based on the decision-theoretical conditions underlying the selection of events for news coverage in science journalism, this article uses a novel input-output analysis to investigate which of the more than eight million scientific study results published between August 2014 and July 2018 have been selected by global journalism to a relevant degree. We are interested in two different structures in the media coverage of scientific results. Firstly, the structure of sources that journalists use, i.e. scientific journals, and secondly, the congruence of the journalistic selection of single results. Previous research suggests that the selection of sources and results follows a certain heavy-tailed distribution, a power law. Mathematically, this distribution can be described with a function of the form C*x-α. We argue that the exponent of such power law distributions can potentially be an indicator to describe selectivity in journalism on a high aggregation level. In our input-output analysis, we look for such patterns in the coverage of all scientific results published in the database Scopus over four years. To get an estimate of the coverage of these results, we use data from the altmetrics provider Altmetric, more precisely their Mainstream-Media-Score (MSM-Score). Based on exploratory analyses, we define papers with a score of 50 or above as Social Impact Papers (SIPs). Over our study period, we identified 5,833 SIPs published in 1,236 journals. For both the distribution of the source selection and the distribution of the selection of single results, an exponentially truncated power law is a better fit than the power law, mostly because we find a steeper decline in the tail of the distributions.