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Introduction. Meaning-in-life orientations and self-reflection are important indicators of the regulation of personal meanings. In a modern rapidly changing society, individuals do not fully manage their lives due to the influence of various uncertainty factors. The COVID-19 pandemic that began in early 2020 and swept across the world is considered a global factor of uncertainty. The increased uncertainty of life associated with the pandemic is an additional stressor. This paper represents a first effort in obtaining empirical data on gender characteristics of teachers’ meaning-based regulation in the context of the coronavirus pandemic. This paper reports the results of an empirical study of differences in meaning-in-life orientations and self-reflection among teachers before and during the pandemic as different conditions of information uncertainty in their lives. Methods. The study was conducted among female teachers of secondary schools in the Rostov region in March 2017 (n = 53) and in October 2020 (n = 43), i.e. before and during the pandemic, which can be considered different conditions of information uncertainty in their lives. We used the following psychological assessment tools: (a) the Meaning-in-Life Orientations test by D. A. Leont'ev and (b) the Self-reflection Questionnaire by I. A. Stetsenko. Results. We observed an increase in all means of both tests during the pandemic, compared to the pre-pandemic values. Differences in all parameters are statistically significant (according to the Mann–Whitney U test). Discussion. Our findings indicate that the pandemic represents a surmountable stressor for the homogeneous acmeological category of female teachers surveyed in this study. Information uncertainty contributes to constructive restructuring of the processes of meaning-based regulation and psychological adaptation, which leads to an increase in the level of meaningfulness of life orientations and self-reflection among the respondents.

Accepted by: Aris Syntetos Exploring the effects of information uncertainty presents an extensive challenge to decision makers. This study presents a set of behavioural experiments that examine the impact of incomplete information on newsvendor decisions. Findings show that orders deviate from normative benchmarks when decision makers have incomplete information and this tendency is stronger when the demand distribution is not known. Comparison of decisions under incomplete information against behavioural benchmarks with full and no information reveal that the availability of price and cost information brings decisions significantly closer to normative levels when the underlying demand distribution is unknown. On the opposite spectrum, when demand information is available, not knowing price or cost does not lead to worse decisions. Analysing newsvendor profits under various information conditions, we find participants capture at most 84% of earnings they could have generated if they ordered the normative quantity in high-profit margin settings; the corresponding percentage is 51% in low-profit margin settings. Our results suggest decreasing uncertainty on the demand distribution has a consistently positive impact on profits, while uncertainty about cost or price does not have a significant effect. Implications of our findings on the differential impact of incomplete information are discussed via the backdrop of the prevalence of newsvendor framework across a wide range of operational decisions. [ABSTRACT FROM AUTHOR]

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Background: Using federal funds from the 2009 Health Information Technology for Economic and Clinical Health Act, the Centers for Medicare and Medicaid Services funded the 2011-2021 Medicaid electronic health record (EHR) incentive programs throughout the country., Objective: Identify the market factors associated with Meaningful Use (MU) of EHRs after primary care providers (PCPs) enrolled in the Florida-EHR incentives program through Adopting, Improving, or Upgrading (AIU) an EHR technology., Research Design: Retrospective cohort study using 2011-2018 program records for 8464 Medicaid providers., Main Outcome: MU achievement after first-year incentives., Independent Variables: The resource dependence theory and the information uncertainty perspective were used to generate key-independent variables, including the county's rurality, educational attainment, poverty, health maintenance organization penetration, and number of PCPs per capita., Analytical Approach: All the county rates were converted into 3 dichotomous measures corresponding to high, medium, and low terciles. Descriptive and bivariate statistics were calculated. A generalized hierarchical linear model was used because MU data were clustered at the county level (level 2) and measured at the practice level (level 1)., Results: Overall, 41.9% of Florida Medicaid providers achieved MU after receiving first-year incentives. Rurality was positively associated with MU ( P <0.001). Significant differences in MU achievements were obtained when we compared the "high" terciles with the "low" terciles for poverty rates ( P =0.002), health maintenance organization penetration rates ( P =0.02), and number of PCPs per capita ( P =0.01). These relationships were negative., Conclusions: Policy makers and health care managers should not ignore the contribution of market factors in EHR adoption., Competing Interests: The authors declare no conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Abstract Background Individuals’ stress in responding to the current COVID-19 pandemic may be exacerbated by information uncertainty driven by inconsistent, unverified, and conflicting news from various sources. The current study aims to test if information uncertainty during the COVID-19 outbreak was related to acute stress disorder (ASD) over and above other psychosocial stressors. Methods An anonymous online survey was conducted with 7800 college students throughout China from January 31 through February 11, 2020. Existing scales were modified to measure ASD and six potential stressors including information uncertainty during the COVID-19 outbreak. Hierarchical regression analysis was conducted to assess the unique association of information uncertainty with ASD. To minimize the effect of large sample size and also to get a sense of whether the effects of information uncertainty were similar to people at the center of the epidemic, we repeated the hierarchical regression among 10% of the students who were randomly selected from the entire sample (“10% random sample”; n = 780) and 226 students from Hubei Province where the outbreak started. Results Information uncertainty was highly prevalent among the respondents (64%). It was significantly associated with ASD beyond other key variables and potential stressors across three samples. In the hierarchical regression among the entire sample, demographic variables accounted for 9.4% of the variance in ASD. The other five stressors added 5.1% of the variance. The information uncertainty (β = .159; p

Evaluating information quality online is increasingly important for healthy decision‐making. People assess information quality using visual interfaces (e.g., computers, smartphones) with visual cues like aesthetics. Yet, voice interfaces lack critical visual cues for evaluating information because there is often no visual display. Without ways to assess voice‐based information quality, people may overly trust or misinterpret information which can be challenging in high‐risk or sensitive contexts. This paper investigates voice information uncertainty in one high‐risk context—health information seeking. We recruited 30 adults (ages 18–84) in the United States to participate in scenario‐based interviews about health topics. Our findings provide evidence of information uncertainty expectations with voice assistants, voice search preferences, and the audio cues they use to assess information quality. We contribute a nuanced discussion of how to inform more critical information ecosystems with voice technologies and propose ways to design audio cues to help people more quickly assess content quality. [ABSTRACT FROM AUTHOR]

National differences in uncertainty, inequality, and trust have been accentuated by COVID-19. There are indications that the pandemic has impacted societies characterized by high uncertainty, inequality, and low trust harder than societies characterized by low uncertainty, equality, and high trust. This study investigates differential response strategies to COVID-19 as reflected in news media of two otherwise similar low uncertainty societies: Denmark and Sweden. The comparison is made using a recent approach to information dynamics in unstructured data. The main findings are that the news dynamics generally mirror public-health policies, capture fundamental socio-cultural variables related to uncertainty and trust, and may provide a measure of societal uncertainty. The findings can provide insights into evolutionary trajectories of decision-making under high uncertainty and, from a methodological level, be used to develop a media-based index of uncertainty and trust.

This article is devoted to the study of psychological predictors of students’ informational behavior and their peculiarities of over-coming stressful situations. The article reveals the relevance of studying this problem, the degree of study of the topic of informational behavior in various scientific fields. The necessity of studying the motivational basis of informational behavior is substantiated; the analysis of diagnostic approaches to the study of informational behavior is given. The article presents the author’s approach to studying the motivational basis of informational behavior and reveals two groups of informational behavior strategies - normative and risky. The empirical part of the article presents an analysis of the conducted research for the study of psychological predictors of students’ informational behavior in the Internet. It was revealed that the normative strategy of informational behavior “Internet as a motivating force” has the largest number of correlations with coping strategies and indicators of resilience. The results obtained can prove the adaptability of students who actively use the socially oriented strategy “Internet as a motivating force” and the level of their resilience.

The physical-chemical monitoring of vast oceanic areas aims at assessing the status and evolution of the environmental resource for its exploration, protection and/or better understanding. However, the interpretation of monitoring data is affected by ocean seasonality and heterogeneity, and by the quality of sampling and characterization tools used to study the environment. All these factors contribute to the uncertainty of collected information that should be expressed in determined parameter values or trends. A trend of a studied parameter quantified by values difference is significant if the observed absolute value of the difference is larger than their expanded uncertainty. The correlation of studied parameters, useful for their interpretation, is equality affected by the mentioned sources of uncertainty. This work describes the metrologically sound evaluation of trends and correlations of physicochemical parameters of vast oceanic areas where all uncertainty sources affecting the information are considered by simulating their complex impact by the Monte Carlo Method. The described methodology was successfully used to study the impact of summer upwelling in an 800 km 2 coastal area offshore two large cities in Portugal. Nutrients, conductivity, salinity and temperature trends and correlations are distinguished from system heterogeneity, sampling and sample analysis uncertainty for a 99% confidence level., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This study employed deep learning to analyze a substantial data set of 109.13 million COVID-19-related microblogs, leading to the construction of a specialized risk perception indicator dictionary. Employing this dictionary, we were able to capture the dynamic fluctuations in risk perception within online communities across various cities in real time. This approach highlighted the varying intensities of public response to the evolving crisis during the isolation and normalization stages of the pandemic. We observed that COVID-19-related transmission threat and information uncertainty significantly influenced public risk perception at different stages of the pandemic. Innovatively, our study quantifies public psychological resilience within online communities by examining the equilibrium between public risk perception and objective COVID-19-related risks. This equilibrium is conceptualized as the alignment of public perception with the evolving reality of COVID-19 threat and information. We investigated psychological resilience in two dimensions: adaptability, indicated by the extent of deviation from this equilibrium, and agility, reflected in the rate at which equilibrium is reestablished. Our study not only unveils new insights into the intricate relationship among public risk perception, the evolving risks, and psychological resilience but also offers empirical evidence to inform risk management strategies in online communities at different stages of a crisis. Practical Applications: This research provides essential insights into how public perception and emotional responses during health crises like COVID-19 can be monitored and analyzed through social media data. By utilizing advanced analytical methods, including natural language processing (NLP) and panel vector error correction (PVEC) modeling, the study successfully quantified the psychological resilience of online communities. These methods allow for the real-time assessment of how communities adapt and respond to evolving risks, such as transmission threats and information uncertainty. For policymakers and crisis managers, these findings offer valuable tools for understanding public sentiment during different phases of a crisis. This understanding is crucial for shaping effective communication strategies and tailoring public health responses to specific community needs. The study's methodology is adaptable across different cultural contexts and can be applied to various crisis scenarios, making it a versatile tool in global crisis management. The study's approach demonstrates the potential of using social media analytics not only for academic research but also for practical, real-time crisis management. By providing a framework for measuring and interpreting public risk perception and resilience, the research aids in the development of more responsive and effective public health strategies. Overall, this research contributes significantly to both theory and practice in the field of crisis management, offering novel methods for assessing community resilience and guiding decision-making during public health emergencies. [ABSTRACT FROM AUTHOR]

Dong Yeong Shin,1 Robert Weech-Maldonado,2 Jongwha Chang3 1Department of Public Health Sciences, New Mexico State University, Las Cruces, NM, USA; 2Department of Health Services Administration, University of Alabama, Birmingham, AL, USA; 3Department of Pharmaceutical Sciences., University of Texas, El Paso, TX, USACorrespondence: Jongwha Chang Department of Pharmaceutical SciencesSchool of Pharmacy, University of Texas at El Paso, 500 W. University Ave, El Paso, TX 79968, USATel +1 915-747-8241Email jchang@utep.eduPurpose: Due to a limited number of studies with generalizable findings on the relationships between market conditions and RN staffing levels in hospitals, this study examined such relationships employing a longitudinal design with a representative national sample.Materials and Methods: We used longitudinal panel datasets from 2006 to 2010, drawn from various datasets including the American Hospital Association Annual Survey Database and the Area Health Resource File. A random-effects linear regression model was used to measure the influence of market conditions on RN staffing levels.Results: The results of this study showed that market conditions were significantly associated with RN staffing levels in hospitals. First, an increase in per capita income and being located in urban rather than rural areas were associated with a greater number of RNs per 1,000 inpatient days and a higher ratio of RNs to LPNs and nursing aides. In addition, an increase in the number of physician specialists was associated with an increase in the number of RNs per 1,000 inpatient days. Second, an increase in Medicare HMO penetration in the environment was related to an increase in the RNs to LPNs and nursing aides ratio. Lastly, an increase in market competition was associated with an increase in the number RNs per 1,000 inpatient days and the ratio of RNs to LPNs and nursing aides.Conclusion: The findings of this study suggest that staffing decision makers in hospitals should consider how to best align their RN staffing levels with their operating environment. In addition, health policy makers may improve the levels the RN supply in communities that needs more RNs by modulating external environmental forces (eg, specialist resources) that influence RN staffing levels in hospitals.Keywords: market condition, external environment, munificence, dynamism, complexity, RN staffing, hospital

As the negative environmental impacts of transportation systems become more severe, governments and environmental groups are seeking more sustainable transportation options, such as replacing fuel-powered vehicles with electric vehicles and expanding public transportation systems to reduce the number of people driving on their own, in order to reduce the environmental impacts of transportation systems. At present, the rapid expansion of public transportation systems is not an easy task and requires a long period of time to plan for expansion and construction, so people are increasingly looking to find means of transportation that meet sustainable conditions as solutions. In this context, electric bicycles are one of the solutions that people can choose, with benefits such as energy saving, carbon reduction, effective air pollution reduction, and simple and labor-saving riding. However, in Taiwan, despite the many benefits of electric bicycles, their popularity is not high. Therefore, this study focuses on the factors that affect the purchase of electric bicycles in Taiwan. The Influential Network Relation Map (INRM) generated by the Z-based Decision-making Trial and Evaluation Laboratory (Z-DEMATEL) technique is used to describe the influence relationships among the factors and to establish the key evaluation criteria of electric bicycle purchase intention. The results indicate that vehicle price, safety, motor performance, battery life, and battery durability are the most important factors in purchasing electric bicycles. Furthermore, the power of motor is considered as the factor that most significantly affects other criteria, while safety and price are most likely to be affected by other criteria. This study has contributed to academia and industry, for the dependency weights of these factors are set to provide a scientific and systematic way to show how consumers think in the decision-making process and to provide more reliable information and management implications for the electric bicycle industry.

This case report describes entropy based on statistics of the novel coronavirus infection in the Russian Federation. Entropy (in bits) determined on the basis of the ratio of patients with clinical manifestations of the disease and asymptomatic carrier state, increased from 0.7219 by April 1, 2020, when the ratio of such patients was 4/5 to 1/5, to 0.8813 by April 14, 2020, when the ratio of the two groups of patients was 7/10 to 3/10. On May 3, 2020, the ratio of the two groups leveled off, reaching ½ to ½, so the entropy was 1.0000. An analysis of the data from April 19, 2020 to June 1, 2020 allowed us to clarify the average proportion between patients with and without symptoms, which is 14/25 to 11/25 with a certain bias towards patients with clinical manifestations of the novel coronavirus infection, and the entropy for this ratio is 0.9896. We found a transformation of entropy of the novel coronavirus infection which is explained by different proportions of patients with and without symptoms of the disease.