COVID-19 and Dr. Google: Parents' changing experience using online health information about their children's health during the pandemic.

AIMS: To identify how the COVID-19 pandemic influences parents' use of the internet, including social media, when seeking health-related information about the pandemic relevant to their children. METHODS: This study employed semi-structured interviews to explore the factors affecting parents of young children when information-seeking online about their children's health related to the COVID-19 pandemic. Parents of children with and without chronic health conditions were interviewed in July and August 2020. Interviews were audio-recorded and transcribed verbatim, then analysed using theoretical thematic analysis, based on Social Cognitive Theory. RESULTS: Through interviews with 13 parents, we identified a myriad of factors that affected parents' internet searching. The decision to access online health information and the regulation of its usage was multifaceted and relied upon the interactions between environmental triggers and parents' information needs, personal attitudes, and circumstances. Overall, parents felt supported by online health information during the COVID-19 pandemic, and the majority were confident in their ability to navigate the plethora of online health information. However, parents of children with chronic conditions had unmet information needs in relation to COVID-19 and their children's condition. CONCLUSIONS: Understanding parents' attitudes and behaviours when seeking online health information that is relevant to their children during a global pandemic can inform the optimisation of online health content delivery to parents.

Blended and e-learning in pediatric education: harnessing lessons learned from the COVID-19 pandemic.

Face-to-face education as the traditional basis for medical education was disrupted by the COVID-19 pandemic as learners and educators were moved online with little time for preparation. Fortunately, as online learning has grown, together with medical education shifting to problem-based and team-centered learning over the last three decades, existing resources have been adapted and improved upon to meet the challenges. Effective blended learning has resulted in innovative synchronous and asynchronous learning platforms. Clearly, to do this well requires time, effort, and adjustment from clinicians, educators, and learners, but it should result in an engaging change in teaching practice. Its success will rely on an evaluation of learning outcomes, educator and learner satisfaction, and long-term retention of knowledge. It will be important to maintain ongoing assessment of all aspects of the medical education process, including how to best teach and assess theory, physiology, pathology, history-taking, physical examination, and clinical management.Conclusion: The COVID-19 pandemic triggered emergency transitional processes for teaching and assessment in medical education which built upon existing innovations in teaching medicine with the use of technology. These strategies will continue to evolve so as to provide the basis for an enduring hybrid teaching model involving blended and e-learning in medical education.. What is Known: • Most pediatricians provide clinical teaching to medical students and residents, but few have had formal training in online educational approaches and techniques. • Being able to adapt to new and innovative integrated teaching methods is of key importance when becoming a competent teacher. What is New: • This review presents an up-to-date summary of best practice in blended and e-learning and how it may be optimally delivered. • Knowledge of the principles of e-learning, and how people learn more generally, helps pediatricians shape their clinical teaching and facilitates better interaction with medical students and residents.

Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach.

OBJECTIVE: Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach. DESIGN: This retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs. SETTING AND PARTICIPANTS: The model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California. METHODS: Model features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets. RESULTS: The mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor. CONCLUSIONS AND IMPLICATIONS: A machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection.