Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or in response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of VIRA. We use this framework to report baseline intent-discovery results over VIRADialogs, that highlight the difficulty of this task. © 2023 Association for Computational Linguistics.

Assessment of integrated patterns of human-animal-environment health: a holistic and stratified analysis.

BACKGROUND: Data-driven research is a very important component of One Health. As the core part of the global One Health index (GOHI), the global One Health Intrinsic Drivers index (IDI) is a framework for evaluating the baseline conditions of human-animal-environment health. This study aims to assess the global performance in terms of GOH-IDI, compare it across different World Bank regions, and analyze the relationships between GOH-IDI and national economic levels. METHODS: The raw data among 146 countries were collected from authoritative databases and official reports in November 2021. Descriptive statistical analysis, data visualization and manipulation, Shapiro normality test and ridge maps were used to evaluate and identify the spatial and classificatory distribution of GOH-IDI. This paper uses the World Bank regional classification and the World Bank income groups to analyse the relationship between GOH-IDI and regional economic levels, and completes the case studies of representative countries. RESULTS: The performance of One Health Intrinsic Driver in 146 countries was evaluated. The mean (standard deviation, SD) score of GOH-IDI is 54.05 (4.95). The values (mean SD) of different regions are North America (60.44, 2.36), Europe and Central Asia (57.73, 3.29), Middle East and North Africa (57.02, 2.56), East Asia and Pacific (53.87, 5.22), Latin America and the Caribbean (53.75, 2.20), South Asia (52.45, 2.61) and sub-Saharan Africa (48.27, 2.48). Gross national income per capita was moderately correlated with GOH-IDI (R2 = 0.651, Deviance explained = 66.6%, P < 0.005). Low income countries have the best performance in some secondary indicators, including Non-communicable Diseases and Mental Health and Health risks. Five indicators are not statistically different at each economic level, including Animal Epidemic Disease, Animal Biodiversity, Air Quality and Climate Change, Land Resources and Environmental Biodiversity. CONCLUSIONS: The GOH-IDI is a crucial tool to evaluate the situation of One Health. There are inter-regional differences in GOH-IDI significantly at the worldwide level. The best performing region for GOH-IDI was North America and the worst was sub-Saharan Africa. There is a positive correlation between the GOH-IDI and country economic status, with high-income countries performing well in most indicators. GOH-IDI facilitates researchers' understanding of the multidimensional situation in each country and invests more attention in scientific questions that need to be addressed urgently.

Integrated quantitative evaluation framework of sustainable development – The complex case of the Yangtze River Delta

Sustainable development is central to the current societal functioning, whose complexity demands consideration on a regional scale. However, there are disparate methods to express sustainable development, many of which use qualitative analysis cumbersome for policy-makers. Previous studies focused on environmental, economic, and social impacts without fully considering the regulation mechanisms of the plethora of administrative bodies. To fill this research gap, this research establishes an integrated assessment framework involving four pillars: environment and ecology, society and culture, economics, and governance and policy. Further, indicator systems and quantitative analysis give comparable and objective results. The current study applied them to one of the most economically significant and developed Chinese regions, the Yangtze River Delta. The result shows a dynamic variation in regional sustainability from 2010 to 2019, indicating an annual increase. Although economic and societal development has been increasing steadily, environmental development has stagnated in the past two years, and the influencing policy has fluctuated dramatically. Our analysis was done in Shanghai, Jiangsu, Zhejiang, and Anhui. Even though all regions showed increasing sustainability, we observed an imbalance in regional sustainable development. Achieving a regional approach and enhanced regional coordination in the Yangtze River Delta is imperative and cannot be ignored by local, regional, and national policy-makers. More importantly, this study created a model capable of predicting the impact of the COVID-19 epidemic on regional sustainable development. The model showed that, compared with predicted values, a 6.65% decrease in the integrated sustainability index ensued, attributed to the pandemic in Zhejiang province. © 2022 Elsevier Ltd

A method for comparing multiple imputation techniques: A case study on the U.S. national COVID cohort collaborative.

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful for assessing associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases, whose removal may introduce severe bias. Several multiple imputation algorithms have been proposed to attempt to recover the missing information under an assumed missingness mechanism. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithm works best in a given scenario. Furthermore, the selection of each algorithm's parameters and data-related modeling choices are also both crucial and challenging. In this paper we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. Extensive experiments show that our approach can effectively highlight the most promising and performant missing-data handling strategy for our case study. Moreover, our methodology allowed a better understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

Designing evaluation framework for the empirical assessment of COVID-19 mobile apps in Pakistan.

The significant proliferation in the mobile health applications (Apps) amidst Coronaviruses disease 2019 (COVID-19) resulted in decision making problems for healthcare professionals, decision makers and mobile users in Pakistan. This decision making process is also hampered by mobile app trade-offs, multiple features support, evolving healthcare needs and varying vendors. In this regard, evaluation model for mobile apps is presented which completes in three different phases. In first phase, features-based criteria is designed by leveraging Delphi method, and twenty (20) mobile apps are selected from app stores. In second stage, empirical evaluation is performed by using hybrid multi criteria decision approaches like CRiteria Importance Through Inter-criteria Correlation (CRITIC) method has been used for assigning weights to criteria features; and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method has been used for assessment of mobile app alternatives. In last step, decision making is performed to select the best mobile app for COVID-19 situations. The results suggest that proposed model can be adopted as a guideline by mobile app subscribers, patients and healthcare professionals.

Mindfulness and Understanding of Self-Care for Leaders of Extension: Promoting Well-Being for Health Educators and Their Clients.

Background: Mindfulness and self-care, practiced through a variety of methods like meditation and exercise, can improve overall sense of holistic well-being (i.e., flourishing). Increasing mindfulness and self-care may lead to increased flourishing and job satisfaction among the nation-wide Cooperative Extension system delivery personnel (agents) through a theory-based online program and an extended experiential program. Methods: Cooperative Extension agents from two states were invited to participate in MUSCLE via statewide listservs. Participants were invited to attend sessions and complete competency checks and between-session assignments each week. The study was conducted using Zoom. Pre- and post- program surveys included validated scales for flourishing and physical activity status. Due to high demand for mindfulness programing during the onset of the COVID-19 pandemic, experiential "Mindful Meet-up" 30-minute sessions were held on Zoom. Dissemination and implementation of the two differing interventions (i.e., MUSCLE and Mindful Meet-ups) were examined. Results: MUSCLE (more intensive program with assignments and competency checks) had lower reach, and did not show statistically increased flourishing or physical activity. Mindful Meet-ups had higher attendance and proportional reach during the beginning of the pandemic, but no practical measure of flourishing or physical activity behaviors. Unsolicited qualitative feedback was encouraging because the interventions were well-received and participants felt as though they were more mindful. Conclusions: While agents anecdotally reported personal improvements, capturing data on outcomes was challenging. Complementing outcome data with implementation and dissemination outcomes allowed for a richer picture to inform intervention decision-making (i.e., offering the same or new programming depending on participant needs).

COVID-19 Prediction using LSTM

As COVID-19 has transformed into a pandemic, the pollution, disasters, and ramifications for the economy have turned out to be indisputable. Sensible systems ought to be used to evaluate the money related impact of future disease guides to restrict fear and dubiousness about COVID-19 pandemic's monetary impact. Gotten from Epidemics already (like influenza) and monetary examples, this assessment gathered a plague affliction evaluation framework and a money related circumstance estimate model. Using this methodology, the author moreover guesses the monetary aftereffects of future COVID-19 spread. The disclosures of the audit are according to the accompanying. In any case, the significant learning-based monetary effect assumption model was attempted with really look at data to ensure that it actually expected development rates by percent. Second, that used a significant learning-based compelling disease money related impact estimate model, the makers present the COVID- 19 example and future financial effect assumption results for the looming year. At the present time, a large portion of COVID- 19 assessment is on method for managing drug spread using quantifiable mathematical estimations. This work will be used as a definite reference for compelling and preventive bearing by expecting the spread of diseases and monetary issues related with COVID-19 using significant learning advancement and credible overpowering ailment data. © 2022 IEEE.

Development of an Open Government Data (OGD) Evaluation Framework for BIM

Open government data (OGD) provide an opportunity for developing various services by disclosing information monopolized by the government to the public so that the private sector can use it. The private sector is utilizing this to improve the work efficiency and productivity by collecting, analyzing, and reprocessing OGD for various work steps of a BIM-based design project. However, most studies on OGD focus on the functionality and usability of data portals and the factors for evaluating the data itself such as openness, accountability, and transparency. This study aims to provide an evaluation framework for OGD for the AEC industry to assess the data utilization environment in order to improve the productivity of BIM-based projects. Several OGD principles found within related literature are discussed, and from them we extract evaluation framework levels. Then, we validate the proposed framework by applying it to a case of developing a BIM-based design support system using OGD datasets. This research concludes by suggesting that to effectively utilize OGD in the construction industry, the private sector should simply view data after collecting them, create an institutional environment for creating new values by reprocessing data, and build an active data utilization roadmap based on this environment.

A Utility Framework for COVID-19 Online Forward Triage Tools: A Swiss Telehealth Case Study.

The SARS-CoV-2 pandemic caused a surge in online tools commonly known as symptom checkers. The purpose of these symptom checkers was mostly to reduce the health system burden by providing worried people with testing criteria, where to test and how to self-care. Technical, usability and organizational challenges with regard to online forward triage tools have also been reported. Very few of these online forward triage tools have been evaluated. Evidence for decision frameworks may be of particular value in a pandemic setting where time frames are restricted, uncertainties are ubiquitous and the evidence base is changing rapidly. The objective was to develop a framework to evaluate the utility of COVID-19 online forward triage tools. The development of the online forward triage tool utility framework was conducted in three phases. The process was guided by the socio-ecological framework for adherence that states that patient (individual), societal and broader structural factors affect adherence to the tool. In a further step, pragmatic incorporation of themes on the utility of online forward triage tools that emerged from our study as well as from the literature was performed. Seven criteria emerged; tool accessibility, reliability as an information source, medical decision-making aid, allaying fear and anxiety, health system burden reduction, onward forward transmission reduction and systems thinking (usefulness in capacity building, planning and resource allocation, e.g., tests and personal protective equipment). This framework is intended to be a starting point and a generic tool that can be adapted to other online forward triage tools beyond COVID-19. A COVID-19 online forward triage tool meeting all seven criteria can be regarded as fit for purpose. How useful an OFTT is depends on its context and purpose.

A framework for evaluating SDOH curriculum integration.

Social determinants of health (SDOH) directly contribute to health inequities among populations and communities. These structural and social forces impact health and health outcomes. Nurses play a vital role in addressing the SDOH and closing gaps relative to disparate outcomes. Integration of SDOH in nursing curriculums has become highly prioritized in nursing education as marginalized communities continue to experience inequities in health, which have been highlighted during the COVID pandemic. Many schools of nursing have embedded SDOH in course content throughout curricula but lack a structured approach to appraise the effectiveness of incorporating these concepts. This paper describes a framework used to evaluate SDOH integration in pre-and post-licensure curriculum.