Instructional media selection principles for online medical education and emerging models for the new normal.

The COVID-19 pandemic, and the resulting need to avoid in-person classes, compelled many faculty members to convert to a completely online instructional format. The literature on selecting media for medical educators, however, provided little assistance for them to make choices that facilitated learning through using alternative online instruction practices. In this study, we addressed the lack of guidance for the use of media to facilitate the effective online medical education. To optimise the transition from face-to-face educational modalities to online learning, we incorporated insights from theories of media synchronicity and learning. We considered the value of existing learning theories in influencing how we could guide entrenched face-to-face educators to online learning practice. Therefore, we employed existing theories and practice to assist in developing an algorithmic approach to guiding these educators. We reassessed the way taxonomies of learning objectives, practice-oriented learning experiences, the social and collaborative features of learning activities, and media synchronicity theory could have augmented face-to-face teaching, and influenced how these could be reconfigured to assist in the transition to online learning. Consequently, we have developed key principles to inform the continuity of design and selection of instructional media in the transition to medical online learning. We have constructed specific criteria for media selection that correspond to the 12 goals of medical learning. We found that the majority of the goals can be more enhanced by synchronous media than asynchronous versions. We discuss the role of instructional media in emergency online medical education as well as emerging models of media selection for the new normal in medical education and future directions for medical education media research.

The Memphis Pandemic Health Informatics System (MEMPHI-SYS)-Creating a Metropolitan COVID-19 Data Registry Linked Directly to Community Testing to Enhance Population Health Surveillance.

The current coronavirus disease (COVID-19) pandemic has placed unprecedented strain on underfunded public health resources in the Southeastern United States. The Memphis, TN, metropolitan region has lacked infrastructure for health data exchange.This manuscript describes a multidisciplinary initiative to create a community-focused COVID-19 data registry, the Memphis Pandemic Health Informatics System (MEMPHI-SYS). MEMPHI-SYS leverages test result data updated directly from community-based testing sites, as well as a full complement of public health data sets and knowledge-based informatics. It has been guided by relationships with community stakeholders and is managed alongside the largest publicly funded community-based COVID-19 testing response in the Mid-South. MEMPHI-SYS has supported interactive Web-based analytic resources and informs federally funded COVID-19 outreach directed toward neighborhoods most in need of pandemic support.MEMPHI-SYS provides an instructive case study of how to collaboratively establish the technical scaffolding and human relationships necessary for data-driven, health equity-focused pandemic surveillance, and policy interventions.

Defining Radiation Treatment Interruption Rates During the COVID-19 Pandemic: Findings From an Academic Center in an Underserved Urban Setting.

PURPOSE: Our purpose was to characterize radiation treatment interruption (RTI) rates and their potential association with sociodemographic variables in an urban population before and during the COVID-19 pandemic. METHODS AND MATERIALS: Electronic health records were retrospectively reviewed for patients treated between January 1, 2015, and February 28, 2021. Major and minor RTI were defined as ≥5 and 2 to 4 unplanned cancellations, respectively. RTI was compared across demographic and clinical factors and whether treatment started before or after COVID-19 onset (March 15, 2020) using multivariate logistic regression analysis. RESULTS: Of 2240 study cohort patients, 1938 started treatment before COVID-19 and 302 started after. Patient census fell 36% over the year after COVID-19 onset. RTI rates remained stable or trended downward, although subtle shifts in association with social and treatment factors were observed on univariate and multivariate analysis. Interaction of treatment timing with risk factors was modest and limited to treatment length and minor RTI. Despite the stability of cohort-level findings showing limited associations with race, geospatial mapping demonstrated a discrete geographic shift in elevated RTI toward Black, underinsured patients living in inner urban communities. Affected neighborhoods could not be predicted quantitatively by local COVID-19 transmission activity or social vulnerability indices. CONCLUSIONS: This is the first United States institutional report to describe radiation therapy referral volume and interruption patterns during the year after pandemic onset. Patient referral volumes did not fully recover from an initial steep decline, but local RTI rates and associated risk factors remained mostly stable. Geospatial mapping suggested migration of RTI risk toward marginalized, minority-majority urban ZIP codes, which could not otherwise be predicted by neighborhood-level social vulnerability or pandemic activity. These findings signal that detailed localization of highest-risk communities could help focus radiation therapy access improvement strategies during and after public health emergencies. However, this will require replication to validate and broaden relevance to other settings.

Exploring celebrity influence on public attitude towards the COVID-19 pandemic: social media shared sentiment analysis.

OBJECTIVES: The COVID-19 pandemic has introduced new opportunities for health communication, including an increase in the public's use of online outlets for health-related emotions. People have turned to social media networks to share sentiments related to the impacts of the COVID-19 pandemic. In this paper, we examine the role of social messaging shared by Persons in the Public Eye (ie, athletes, politicians, news personnel, etc) in determining overall public discourse direction. METHODS: We harvested approximately 13 million tweets ranging from 1 January 2020 to 1 March 2022. The sentiment was calculated for each tweet using a fine-tuned DistilRoBERTa model, which was used to compare COVID-19 vaccine-related Twitter posts (tweets) that co-occurred with mentions of People in the Public Eye. RESULTS: Our findings suggest the presence of consistent patterns of emotional content co-occurring with messaging shared by Persons in the Public Eye for the first 2 years of the COVID-19 pandemic influenced public opinion and largely stimulated online public discourse. DISCUSSION: We demonstrate that as the pandemic progressed, public sentiment shared on social networks was shaped by risk perceptions, political ideologies and health-protective behaviours shared by Persons in the Public Eye, often in a negative light. CONCLUSION: We argue that further analysis of public response to various emotions shared by Persons in the Public Eye could provide insight into the role of social media shared sentiment in disease prevention, control and containment for COVID-19 and in response to future disease outbreaks.

Healthcare professionals' perceptions and recommendations regarding adolescent vaccinations in Georgia and Tennessee during the COVID-19 pandemic: A qualitative research.

INTRODUCTION: Despite its benefits, HPV vaccine uptake has been historically lower than other recommended adolescent vaccines in the United States (US). While hesitancy and misinformation have threatened vaccinations for many years, the adverse impacts from COVID-19 pandemic on preventive services have been far-reaching. OBJECTIVES: To explore the perceptions and experiences of adolescent healthcare providers regarding routine vaccination services during the COVID-19 pandemic. METHODOLOGY: Between December 2020 and May 2021, in-depth qualitative interviews were conducted via Zoom video conferencing among a purposively selected, diverse group of adolescent healthcare providers (n = 16) within 5 healthcare practices in the US southeastern states of Georgia and Tennessee. Audio recordings were transcribed verbatim and analyzed using a rapid qualitative analysis framework. Our analysis was guided by the grounded theory and inductive approach. RESULTS: Participants reported that patient-provider communications; effective use of presumptive languaging; provider's continuing education/training; periodic reminders/recall messages; provider's personal conviction on vaccine safety/efficacy; early initiation of HPV vaccination series at 9 years; community partnerships with community health navigators/vaccine champions/vaccine advocates; use of standardized forms/prewritten scripts/standard operating protocols for patient-provider interactions; and vaccine promotion through social media, brochures/posters/pamphlets as well as outreaches to schools and churches served as facilitators to adolescent HPV vaccine uptake. Preventive adolescent services were adversely impacted by the COVID-19 pandemic at all practices. Participants highlighted an initial decrease in patients due to the pandemic, while some practices avoided the distribution of vaccine informational materials due to sanitary concerns. CONCLUSION: As part of a larger study, we provided contextual information to refine an intervention package currently being developed to improve adolescent preventive care provision in healthcare practices. Our results could inform the implementation of comprehensive intervention strategies that improve HPV vaccination rates. Additionally, lessons learned (e.g. optimizing patient- provider interactions) could be adopted to expand COVID-19 vaccine acceptance on a sizable scale.

Applied artificial intelligence in healthcare: Listening to the winds of change in a post-COVID-19 world.

This editorial article aims to highlight advances in artificial intelligence (AI) technologies in five areas: Collaborative AI, Multimodal AI, Human-Centered AI, Equitable AI, and Ethical and Value-based AI in order to cope with future complex socioeconomic and public health issues.

Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study.

BACKGROUND: The emergence of the novel coronavirus (COVID-19) and the necessary separation of populations have led to an unprecedented number of new social media users seeking information related to the pandemic. Currently, with an estimated 4.5 billion users worldwide, social media data offer an opportunity for near real-time analysis of large bodies of text related to disease outbreaks and vaccination. These analyses can be used by officials to develop appropriate public health messaging, digital interventions, educational materials, and policies. OBJECTIVE: Our study investigated and compared public sentiment related to COVID-19 vaccines expressed on 2 popular social media platforms-Reddit and Twitter-harvested from January 1, 2020, to March 1, 2022. METHODS: To accomplish this task, we created a fine-tuned DistilRoBERTa model to predict the sentiments of approximately 9.5 million tweets and 70 thousand Reddit comments. To fine-tune our model, our team manually labeled the sentiment of 3600 tweets and then augmented our data set through back-translation. Text sentiment for each social media platform was then classified with our fine-tuned model using Python programming language and the Hugging Face sentiment analysis pipeline. RESULTS: Our results determined that the average sentiment expressed on Twitter was more negative (5,215,830/9,518,270, 54.8%) than positive, and the sentiment expressed on Reddit was more positive (42,316/67,962, 62.3%) than negative. Although the average sentiment was found to vary between these social media platforms, both platforms displayed similar behavior related to the sentiment shared at key vaccine-related developments during the pandemic. CONCLUSIONS: Considering this similar trend in shared sentiment demonstrated across social media platforms, Twitter and Reddit continue to be valuable data sources that public health officials can use to strengthen vaccine confidence and combat misinformation. As the spread of misinformation poses a range of psychological and psychosocial risks (anxiety and fear, etc), there is an urgency in understanding the public perspective and attitude toward shared falsities. Comprehensive educational delivery systems tailored to a population's expressed sentiments that facilitate digital literacy, health information-seeking behavior, and precision health promotion could aid in clarifying such misinformation.

Vaccine hesitancy and other obstacles to COVID-19 control: lessons from smallpox.

The world confronts today a disease which was unknown as recently as early 2019. Now that there is a safe and effective vaccine against COVID-19, lessons can usefully be drawn from previous well documented vaccination efforts. Of these, the best documented and most successful is the Smallpox Eradication Program (SEP). A review was made of publications by major players in smallpox eradication, respecting the important differences between the disease, this review revealed several points of connection. Cultural factors loomed large both in the eradication of smallpox and progress, to date, in the control of COVID-19. Other points of similarity included political commitment, the set-up of strong surveillance mechanisms, and assurance of uniformly high quality vaccines tested and approved by the World Health Organization. The future of COVID-19 control depends, in part, on lessons learned from previous vaccination efforts. A review of those efforts will avoid repetition of past errors and permit adoption of best practices from the past. Such analyses must, of course, respect the important differences between COVID-19 and smallpox.

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the Berlin clinical criteria. The analysis was conducted on data collected from the Intensive care units (ICU) at Emory Healthcare, Atlanta, GA and University of Tennessee Health Science Center, Memphis, TN and the Cerner® Health Facts Deidentified Database, a multi-site COVID-19 EMR database. The participants in the analysis consisted of adults over 18 years of age. Clinical data from 35,804 patients who developed ARDS and controls were used to generate predictive models that identify risk for ARDS onset up to 12-hours before satisfying the Berlin criteria. We identified salient features from the electronic medical record that predicted respiratory failure among this population. The machine learning algorithm which provided the best performance exhibited AUROC of 0.89 (95% CI = 0.88-0.90), sensitivity of 0.77 (95% CI = 0.75-0.78), specificity 0.85 (95% CI = 085-0.86). Validation performance across two separate health systems (comprising 899 COVID-19 patients) exhibited AUROC of 0.82 (0.81-0.83) and 0.89 (0.87, 0.90). Important features for prediction of ARDS included minimum oxygen saturation (SpO2), standard deviation of the systolic blood pressure (SBP), O2 flow, and maximum respiratory rate over an observational window of 16-hours. Analyzing the performance of the model across various cohorts indicates that the model performed best among a younger age group (18-40) (AUROC = 0.93 [0.92-0.94]), compared to an older age group (80+) (AUROC = 0.81 [0.81-0.82]). The model performance was comparable on both male and female groups, but performed significantly better on the severe ARDS group compared to the mild and moderate groups. The eARDS system demonstrated robust performance for predicting COVID19 patients who developed ARDS at least 12-hours before the Berlin clinical criteria, across two independent health systems.

Conceptualization of college students' COVID-19 related mask-wearing behaviors using the Multi-Theory Model of health behavior change.

Background: Recommendations and policies, regarding the use of face coverings, have been instituted to control transmission of coronavirus disease 2019 (COVID-19). Understanding of psychosocial factors related to the use of face coverings within the context of COVID-19 is needed. This study aimed to conceptualize mask-wearing behavior among students using the Multi-theory Model (MTM) of behavior change. Methods: In October 2020, students (n = 595) enrolled in a large public southeastern US university were recruited to participate in a cross-sectional survey, using a valid and reliable instrument. Univariate, bivariate, and multivariate techniques described mask-wearing behavior and differentiated theoretical drivers of mask-wearing between individuals compliant and non-compliant with guidelines. Results: Compliant individuals reported significantly higher scores (P <0.05) for initiation and sustenance of mask-wearing, participatory dialogue, behavioral confidence, emotional transformation, practice for change, changes in the social environment, and significantly lower scores for disadvantage. Among multivariable models, all theoretical predictors exhibited significant relationships to their respective outcomes (initiation and sustenance). Specifically, MTM constructs explained approximately 35% of variance in initiation (R2 = 0.346, F(3,526) = 94.32, P <0.001) and 33% of variance in sustenance of mask wearing (R2 = 0.328, F(3,529) = 87.71, P <0.001) for compliant individuals. Behavioral confidence and emotional transformation exhibited the strongest relationships to initiation (ß = 0.403, P <0.001) and sustenance (ß = 0.450, P <0.001), respectively. Conclusion: Findings suggest a need to design educational programming based on the MTM to promote mask-wearing behavior among laggards who defy face mask guidelines, recommendations, and mandates.

Addressing Parental Vaccine Hesitancy and Other Barriers to Childhood/Adolescent Vaccination Uptake During the Coronavirus (COVID-19) Pandemic.

Routine childhood immunizations are proven to be one of the most effective public health interventions at controlling numerous deadly diseases. Therefore, the CDC recommends routine immunizations for children and adolescent populations against vaccine-preventable diseases e.g., tetanus, pertussis, diphtheria, etc. This current review sought to examine barriers to pediatric vaccine uptake behaviors during the COVID-19 pandemic. We also explored the implications for parental vaccine hesitancy/delay during an ongoing health crisis and proposed recommendations for increasing vaccine confidence and compliance. Our review determined that the receipt for vaccinations steadily improved in the last decade for both the United States and Tennessee. However, this incremental progress has been forestalled by the COVID-19 pandemic and other barriers i.e. parental vaccine hesitancy, social determinants of health (SDoH) inequalities, etc. which further exacerbate vaccination disparities. Moreover, non-compliance to routine vaccinations could cause an outbreak of diseases, thereby, worsening the ongoing health crisis and already strained health care system. Healthcare providers are uniquely positioned to offer effective recommendations with presumptive languaging to increase vaccination rates, as well as, address parental vaccine hesitancy. Best practices that incorporate healthcare providers' quality improvement coaching, vaccination reminder recall systems, adherence to standardized safety protocols (physical distancing, hand hygiene practices, etc.), as well as, offer telehealth and outdoor/drive-through/curbside vaccination services, etc. are warranted. Additionally, a concerted effort should be made to utilize public health surveillance systems to collect, analyze, and interpret data, thereby, ensuring the dissemination of timely, accurate health information for effective health policy decision-making e.g., vaccine distribution, etc.

Explaining Handwashing Behavior in a Sample of College Students during COVID-19 Pandemic Using the Multi-Theory Model (MTM) of Health Behavior Change: A Single Institutional Cross-Sectional Survey.

Amidst the COVID-19 pandemic, handwashing offers a simple and effective hygienic measure for disease prevention. Reportedly, a significant proportion of college students did not follow handwashing recommendations provided by the Centers for Disease Control and Prevention (CDC) in the pre-COVID era. The purpose of this cross-sectional study was to explore and explain the handwashing behavior among college students during the COVID-19 pandemic using a contemporary fourth-generation multi-theory model (MTM) of health behavior change. Data were collected from 713 college students at a large public university in the Southern U.S. in October 2020 using a validated 36-item survey. Statistical analyses included independent samples t-tests, Pearson correlation, and hierarchical regression modeling. Among students not following handwashing recommendations, the constructs of participatory dialogue (ß = 0.152; p < 0.05) and behavioral confidence (ß = 0.474; p < 0.0001) were statistically significant and accounted for 27.2% of the variance in the likelihood of initiation of the behavior. Additionally, the constructs of emotional transformation (ß = 0.330; p < 0.0001), practice for change (ß = 0.296; p < 0.0001), and changes in the social environment (ß = 0.180; p < 0.05) were statistically significant and accounted for 45.1% of the variance in the likelihood of sustaining handwashing behavior. This study highlights the applicability and usability of the MTM in designing and testing behavior change interventions and media messaging in campaigns targeting college students.

Implementing an Urban Public Health Observatory for (Near) Real-Time Surveillance for the COVID-19 Pandemic.

The COVID-19 pandemic is broadly undercutting global health and economies, while disproportionally impacting socially disadvantaged populations. An impactful pandemic surveillance solution must draw from multi-dimensional integration of social determinants of health (SDoH) to contextually inform traditional epidemiological factors. In this article, we describe an Urban Public Health Observatory (UPHO) model which we have put into action in a mid-sized U.S. metropolitan region to provide near real-time analysis and dashboarding of ongoing COVID-19 conditions. Our goal is to illuminate associations between SDoH factors and downstream pandemic health outcomes to inform specific policy decisions and public health planning.