This ‘cohort profile’ aims to provide a description of the study design, methodology, and baseline characteristics of the participants in the Corona Behavioral Unit cohort. This cohort was established in response to the COVID-19 pandemic by the Dutch National Institute for Public Health and the Environment (RIVM) and the regional public health services. The aim was to investigate adherence of and support for COVID-19 prevention measures, psychosocial determinants of COVID-19 behaviors, well-being, COVID-19 vaccination, and media use. The cohort also examined specific motivations and beliefs, such as for vaccination, which were collected through either closed-ended items or open text responses. In April 2020, 89,943 participants aged 16 years and older were recruited from existing nation-wide panels. Between May 2020 and September 2022, 99,676 additional participants were recruited through online social media platforms and mailing lists of higher education organizations. Participants who consented were initially invited every three weeks (5 rounds), then every six weeks (13 rounds), and since the summer of 2022 every 12 weeks (3 rounds). To date, 66% of participants were female, 30% were 39 years and younger, and 54% completed two or more questionnaires, with an average of 9.2 (SD = 5.7) questionnaires. The Corona Behavioral Unit COVID-19 cohort has published detailed insights into longitudinal patterns of COVID-19 related behaviors, support of COVID-19 preventive measures, as well as peoples’ mental wellbeing in relation to the stringency of these measures. The results have informed COVID-19 policy making and pandemic communication in the Netherlands throughout the COVID-19 pandemic. The cohort data will continuously be used to examine COVID-19 related outcomes for scientific analyses, as well as to inform future pandemic preparedness plans.
MULTIFILE
Although stressors are frequently linked to several negative health outcomes, experiencing stressors may be necessary for enhancing performance. At present, the literature is lacking a unified, comprehensive framework that accounts for both positive and negative outcomes following stressors. Therefore, we introduce the framework of hormesis, which has been applied in biological research for decades. According to hormesis, small-to-medium doses of a stressor can stimulate an organism's response, while large doses cause detrimental effects. In this article, we argue that these dose-response dynamics can be found in various domains of performance psychology (i.e., eustress and distress, psychological momentum, emotions, motivation, confidence, cognitive performance, training, skill acquisition, adversity, and trauma). Furthermore, hormesis also accounts for the inter- and intra-individual variability commonly found in responses to stressors. Finally, from an applied perspective, leveraging hormesis may stimulate new psychological interventions that mimic the well-known effects of (toxic) vaccinations at the level of behavior.
Routine immunization (RI) of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe. Pakistan being a low-and-middle-income-country (LMIC) has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases (VPDs). For improving RI coverage, a critical need is to establish potential RI defaulters at an early stage, so that appropriate interventions can be targeted towards such population who are identified to be at risk of missing on their scheduled vaccine uptakes. In this paper, a machine learning (ML) based predictive model has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors. The predictive model uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children. The design of predictive model is based on obtaining optimal results across accuracy, specificity, and sensitivity, to ensure model outcomes remain practically relevant to the problem addressed. Further optimization of predictive model is obtained through selection of significant features and removing data bias. Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit. The results showed that the random forest model achieves the optimal accuracy of 81.9% with 83.6% sensitivity and 80.3% specificity. The main determinants of vaccination coverage were found to be vaccine coverage at birth, parental education, and socio-economic conditions of the defaulting group. This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
MULTIFILE
Thermo Fisher Scientific is exploring Augmented & Virtual Reality (AR&VR) applications for electron microscopy and corresponding business cases for future projects.Materials and structural analyses impact our everyday life. From the medicines we take, the vaccines we receive, to the cars we drive, Thermo Fisher Scientific’s electron microscopes, software, and services drive scientific breakthroughs that help solve some of the world’s most difficult challenges. The Central Service Department is driving research related to training and service solutions using AR&VR because it recognises the vast benefits these technologies can offer its customers around the globe.Partner: Thermo Fisher Scientific’s Central Service Department
