Analysis of social and psychological factors influencing the treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019.

Objective To explore the sociodemographic and psychological factors influencing the continuity of treatment of patients with chronic kidney disease under the regular epidemic prevention and control of coronavirus disease 2019 (COVID-19). Methods A total of 277 patients with chronic kidney disease who were admitted to Department of Nephrology, The First Affiliated Hospital of Naval Medical University (Second Military Medical University) from Apr. 2020 to Mar. 2021 were enrolled and divided into 3 groups: non-dialysis group (n=102), hemodialysis (HD) group (n=108), and peritoneal dialysis (PD) group (n=67). All patients were investigated by online and offline questionnaires, including self-designed basic situation questionnaire, self-rating anxiety scale (SAS), and self-rating depression scale (SDS). The general sociodemographic data, anxiety and depression of the 3 groups were compared, and the influence of sociodemographic and psychological factors on the interruption or delay of treatment was analyzed by binary logistic regression model. Results There were significant differences in age distribution, marital status, occupation, medical insurance type, caregiver type, whether there was an urgent need for hospitalization and whether treatment was delayed or interrupted among the 3 groups (all P0.05). The average SAS score of 65 PD patients was 38.15+/-15.83, including 53 (81.5%) patients without anxiety, 7 (10.8%) patients with mild anxiety, and 5 (7.7%) patients with moderate to severe anxiety. The average SAS score of 104 patients in the HD group was 36.86+/-14.03, including 81 (77.9%) patients without anxiety, 18 (17.3%) patients with mild anxiety, and 5 (4.8%) patients with moderate to severe anxiety. There were no significant differences in the mean score of SAS or anxiety severity grading between the 2 groups (both P0.05). The mean SDS scores of 65 PD patients were 53.42+/-13.30, including 22 (33.8%) patients without depression, 21 (32.3%) patients with mild depression, and 22 (33.8%) patients with moderate to severe depression. The mean SDS scores of 104 patients in the HD group were 50.79+/-10.76, including 36 (34.6%) patients without depression, 56 (53.8%) patients with mild depression, and 12 (11.6%) patients with moderate to severe depression. There were no significant differences in mean SDS scores or depression severity grading between the 2 groups (both P0.05). The results of intra-group comparison showed that the incidence and severity of depression were higher than those of anxiety in both groups. Multivariate binary logistic regression analysis showed that high school education level (odds ratio OR=5.618, 95% confidence interval CI) 2.136-14.776, P0.01), and unmarried (OR=6.916, 95% CI 1.441-33.185, P=0.016), divorced (OR= 5.588, 95% CI 1.442-21.664, P=0.013), urgent need for hospitalization (OR=8.655, 95% CI 3.847-19.476, P0.01) could positively promote the continuity of treatment in maintenance dialysis patients under the regular epidemic prevention and control of COVID-19. In the non-dialysis group, no sociodemographic and psychological factors were found to be associated with the interruption or delay of treatment (P0.05). Conclusion Education, marital status, and urgent need for hospitalization are correlated with the continuity of treatment in patients with chronic kidney disease on maintenance dialysis.Copyright © 2022, Second Military Medical University Press. All rights reserved.

PSYCHOLOGICAL DISTRESS OVER 12 MONTHS POST-DIAGNOSIS IN A NATIONAL EARLY INFLAMMATORY ARTHRITIS COHORT

BackgroundIn inflammatory arthritis patients, the concomitant decline of their mental wellbeing is an increasing concern[1,2]. It is important to not only describe the trajectory of psychological distress in early disease stages, but also understand which clinical outcome measures are most associated with these changes.ObjectivesUsing data from the National Early Inflammatory Arthritis Audit (NEIAA), we assessed trends in psychological wellbeing over 12 months after initial diagnosis and mapped these against clinical outcomes to identify significant associations.MethodsNEIAA collects data from patients referred with suspected early inflammatory arthritis in rheumatology services in England and Wales. We used data provided by 20,472 patients eligible for follow-up (diagnosis of inflammatory arthritis) between May 1st, 2018, and April 1st, 2022. Data items included baseline demographics e.g., age and gender, and clinical variables e.g., rheumatic disease comorbidity index (RDCI), DAS28, and patient reported outcomes.Psychological distress was measured by the sum score of Patient Health Questionnaire Anxiety and Depression Screener (PHQ4ADS). Using mixed effects regression models, we analysed the co-variability of PHQ4ADS with demographic factors and clinical outcomes over 12 months. Time was included as a dummy-coded covariant.ResultsThe analysis included 36% of patients (7,378 out of 20,472) who completed the baseline patient outcome survey. In this cohort, PHQ4ADS scores decreased from a baseline average of 4.7 (CI: [4.6, 4.8]) to 2.62 (CI: [2.5, 2.8]) at 12 months post-diagnosis. The proportion of patients screening positive decreased from 50.0% (CI: [48.9, 51.1]) at baseline to 23.8% (CI: [21.8, 25.9]) at 12 months.At baseline, psychological distress correlated significantly with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28 (Figure 1). No significant correlations were found between psychological distress and working diagnosis, seropositivity, or the assessment being recorded after the start of the COVID-19 pandemic. Younger ages were nonlinearly associated with higher distress levels (coefficient per decade: -0.006;p<0.001;CI: [-0.009, -0.003]) (Figure 1a). Distress levels in females were higher than that of males (coefficient: 0.5;p<0.001;CI: [0.4, 0.7]) (Figure 1b). White patients reported lower PHQ4ADS scores compared to non-white patients (coefficient: -0.7;p<0.001;CI: [-1.0, -0.4]) (Figure 1c). Higher distress levels were also associated with higher RDCI (coefficient: 0.2;p<0.001;CI: [0.1, 0.3]) and prior diagnosis of depression (coefficient: 1.8;p<0.001;CI: [1.5, 2.2]) (Figure 1d, 1e). Furthermore, higher baseline DAS28 scores correlated with more severe psychological distress (coefficient: 0.8;p<0.001;CI: [0.7, 0.8]) (Figure 1f).By 12-months, psychological distress decreased significantly overall, which correlated significantly with ethnicity (coefficient: 0.8;p=0.005;CI: [0.3, 1.4]) and baseline DAS28 (coefficient: -0.5;p<0.001;CI: [-0.6, -0.4]). Compared to white patients, the reduction was significantly greater for non-white patients, but the level of distress was no longer different at 12 months (Figure 1c). While those with higher baseline DAS28 showed a greater reduction in psychological distress, the distress levels remained higher at 12 months (Figure 1f).Figure 1.Changes in psychological distress correlated with age, gender, ethnicity, RDCI, prior depression diagnosis, and baseline DAS28.[Figure omitted. See PDF]ConclusionIn this early inflammatory arthritis cohort, mental health burden was high. Age, gender, ethnicity, RDCI, prior depression diagnosis and baseline DAS28 significantly correlated with psychological distress at baseline. Supporting mental health should be a focus of clinical care for this population and it may be beneficial to use an approach that is culturally valid for non-white patients and accounts for multimorbidity.References[1]Euesden, J, et al. Psychosomatic medicine 79.6 (2017): 638.[2]Lwin, MN, et al. Rheumatology and therapy 7.3 (2020): 457-471.AcknowledgementsThe authors would like to thank the Healthcare Quality Improvement Partnership (HQIP) as the commisioner of NEIAA, British Society for Rheumatology as the audit providers, Net Solving as the audit platform developers, and the Wellcome Trust (ST12406) for funding to support L.Z..Disclosure of InterestsLucy Zhao: None declared, James Galloway Speakers bureau: Has received honoraria from AbbVie Celgene, Chugai, Gillead, Janssen, Eli Lilly, Pfizer, Roche, and UCB, Jo Ledingham: None declared, Sarah Gallagher: None declared, Neena Garnavos: None declared, Paul Amlani-Hatcher: None declared, Nicky Wilson: None declared, Lewis Carpenter Consultant of: Statistical consultancy for Pfizer, Kirsty Bannister: None declared, Sam Norton Speakers bureau: Has received honoraria from Janssen and Pfizer.

Understanding the use of digital finance among older internet users in urban China: Evidence from an online convenience sample

Inclusive digital financial services should welcome older populations and make them beneficiaries of the digital and financial revolution. To understand older adults' experience of using digital financial tools, we conducted an online survey of 268 older internet users aged 60 or above from urban areas of 14 Chinese provinces after China's nationwide COVID-19 lockdown in 2021. Our results revealed that older internet surfers were active in digital financial activities and engaged most with activities that were highly compatible with their lifestyles. Active users significantly differed from inactive users in sociodemographics, confirming that a digital divide related to social stratification exists among older internet users. Digital finance active users were also distinguished from inactive users' attitudes and perceptions toward digital finance. Logistic regression results indicated that perceived usefulness, access to proper devices for digital finance, risk perceptions, and perceived exclusion if not using technology were associated with their adoption of these advanced tools. Older adults reported the perceived inconvenience of in-person financial services during the lockdown. They also expressed a willingness to participate in relevant training if provided. The findings of this study could help aging-related practitioners to understand older adults' engagement in digital finance and guide policy and project design in the area of financial inclusion of the aging population.

METHODOLOGY for ASSESSING the FINANCIAL SECURITY of ENTERPRISES in the POST-PANDEMIC PERIOD of DIGITAL ECONOMY

The article considers the approaches to assessing the financial security of enterprises presented in the literature, determines the rsistance of the textile industry of Uzbekistan to the negative impact of the coronavirus pandemic on the basis of statistical data, and reveals a significant differentiation of textile industry enterprises in terms of financial stability. Based on data on small enterprises in the textile industry of Uzbekistan, a method for assessing the financial security of an enterprise in the post-pandemic period is proposed and tested, taking into account the complex influence of non-financial parameters of economic security and assessing the deviations of the economic situation at a given enterprise from the patterns emerging in the relevant segment of the economy. In this research an econometric model was developed to determine the factors affecting the chemical industry and express their interrelationship, based on the conducted econometric analysis, the directions of development in our country were determined. According to the authors, it is necessary to continue these directions in order to ensure the economic security of industry enterprises in the country. © 2022 ACM.

Within-group estimators for unbalanced-panel data regression model of the open unemployment rate data in east kalimantan province

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.

COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

IMPAIRED HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHIES: A CROSS-SECTIONAL ANALYSIS FROM AN INTERNATIONAL E-SURVEY

BackgroundComprehensive and large-scale assessment of health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) worldwide is lacking. The second COVID-19 vaccination in autoimmune disease (COVAD-2) study [1] is an international, multicentre, self-reported e-survey assessing several aspects of COVID-19 infection and vaccination as well as validated patient-reported outcome measures (PROMs) to outline patient experience in various autoimmune diseases (AIDs), with a particular focus on IIMs.ObjectivesTo investigate physical and mental health in a global cohort of IIM patients compared to those with non-IIM autoimmune inflammatory rheumatic diseases (AIRDs), non-rheumatic AIDs (NRAIDs), and those without AIDs (controls), using Patient-Reported Outcome Measurement Information System (PROMIS) global health data obtained from the COVAD-2 survey.MethodsDemographics, AID diagnoses, comorbidities, disease activity, treatments, and PROMs were extracted from the COVAD-2 database. The primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Secondary outcomes included PROMIS physical function short form-10a (PROMIS PF-10a), pain visual analogue scale (VAS), and PROMIS Fatigue-4a scores. Each outcome was compared between IIMs, non-IIM AIRDs, NRAIDs, and controls. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis.ResultsA total of 10,502 complete responses from 1582 IIMs, 4700 non-IIM AIRDs, 545 NRAIDs, and 3675 controls, which accrued as of May 2022, were analysed. Patients with IIMs were older [59±14 (IIMs) vs. 48±14 (non-IIM AIRDs) vs. 45±14 (NRAIDs) vs. 40±14 (controls) years, p<0.001] and more likely to be Caucasian [82.7% (IIMs) vs. 53.2% (non-IIM AIRDs) vs. 62.4% (NRAIDs) vs. 34.5% (controls), p<0.001]. Among IIMs, dermatomyositis (DM) and juvenile DM were the most common (31.4%), followed by inclusion body myositis (IBM) (24.9%). Patients with IIMs were more likely to have comorbidities [68.1% (IIMs) vs. 45.7% (non-IIM AIRDs) vs. 45.1% (NRAIDs) vs. 26.3% (controls), p<0.001] including mental disorders [33.4% (IIMs) vs. 28.2% (non-IIM AIRDs) vs. 28.4% (NRAIDs) vs. 17.9% (controls), p<0.001].GPH median scores were lower in IIMs compared to NRAIDs or controls [13 (interquartile range 10–15) IIMs vs. 13 (11–15) non-IIM AIRDs vs. 15 (13–17) NRAIDs vs. 17 (15–18) controls, p<0.001] and PROMIS PF-10a median scores were the lowest in IIMs [34 (25–43) IIMs vs. 40 (34–46) non-IIM AIRDs vs. 47 (40–50) NRAIDs vs. 49 (45–50) controls, p<0.001]. GMH median scores were lower in AIDs including IIMs compared to controls [13 (10–15) IIMs vs. 13 (10–15) non-IIM AIRDs vs. 13 (11–16) NRAIDs vs. 15 (13–17) controls, p<0.001]. Pain VAS median scores were higher in AIDs compared to controls [3 (1–5) IIMs vs. 4 (2–6) non-IIM AIRDs vs. 2 (0–4) NRAIDs vs. 0 (0–2) controls, p<0.001]. Of note, PROMIS Fatigue-4a median scores were the highest in IIMs [11 (8–14) IIMs vs. 8 (10–14) non-IIM AIRDs vs. 9 (7–13) NRAIDs vs. 7 (4–10) controls, p<0.001].Multivariable regression analysis in IIMs identified older age, male sex, IBM, comorbidities including hypertension and diabetes, active disease, glucocorticoid use, increased pain and fatigue as the independent factors for lower GPH scores, whereas coexistence of interstitial lung disease, mental disorders including anxiety disorder and depression, active disease, increased pain and fatigue were the independent factors for lower GMH scores.ConclusionBoth physical and mental health are significantly impaired in patients with IIMs compared to those with non-IIM AIDs or those without AIDs. Our results call for greater attention to patient-reported experience and comorbidities including mental disorders to provide targeted approaches and optimise global well-being in patients with IIMs.Reference[1]Fazal ZZ, Sen P, Joshi M, et al. COVAD survey 2 long-term outcomes: unmet need and protocol. Rheumatol Int. 2022;42:2151–58.AcknowledgementsThe authors a e grateful to all respondents for completing the questionnaire. The authors also thank The Myositis Association, Myositis India, Myositis UK, the Myositis Global Network, Cure JM, Cure IBM, Sjögren's India Foundation, EULAR PARE for their contribution to the dissemination of the survey. Finally, the authors wish to thank all members of the COVAD study group for their invaluable role in the data collection.Disclosure of InterestsAkira Yoshida: None declared, Yuan Li: None declared, Vahed Maroufy: None declared, Masataka Kuwana Speakers bureau: Boehringer Ingelheim, Ono Pharmaceuticals, AbbVie, Janssen, Astellas, Bayer, Asahi Kasei Pharma, Chugai, Eisai, Mitsubishi Tanabe, Nippon Shinyaku, Pfizer, Consultant of: Corbus, Mochida, Grant/research support from: Boehringer Ingelheim, Ono Pharmaceuticals, Naveen Ravichandran: None declared, Ashima Makol Consultant of: Boehringer-Ingelheim, Parikshit Sen: None declared, James B. Lilleker: None declared, Vishwesh Agarwal: None declared, Sinan Kardes: None declared, Jessica Day Grant/research support from: CSL Limited, Marcin Milchert: None declared, Mrudula Joshi: None declared, Tamer A Gheita: None declared, Babur Salim: None declared, Tsvetelina Velikova: None declared, Abraham Edgar Gracia-Ramos: None declared, Ioannis Parodis Grant/research support from: Amgen, AstraZeneca, Aurinia Pharmaceuticals, Eli Lilly, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis, and F. Hoffmann-La Roche, Elena Nikiphorou Speakers bureau: Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, Eli Lilly, Consultant of: Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, Eli Lilly, Grant/research support from: Pfizer, Eli Lilly, Ai Lyn Tan Speakers bureau: AbbVie, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, UCB, Consultant of: AbbVie, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, UCB, Arvind Nune: None declared, Lorenzo Cavagna: None declared, Miguel A Saavedra Consultant of: AbbVie, GlaxoSmithKline, Samuel Katsuyuki Shinjo: None declared, Nelly Ziade Speakers bureau: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Consultant of: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Grant/research support from: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Johannes Knitza: None declared, Oliver Distler Speakers bureau: AbbVie, Amgen, Bayer, Boehringer Ingelheim, Janssen, Medscape, Novartis, Consultant of: 4P-Pharma, AbbVie, Acceleron, Alcimed, Altavant, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, iQvia, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Redxpharma, Roivant, Sanofi, Topadur, Grant/research support from: AbbVie, Amgen, Boehringer Ingelheim, Kymera, Mitsubishi Tanabe, Novartis, Roche, Hector Chinoy Grant/research support from: Eli Lilly, UCB, Vikas Agarwal: None declared, Rohit Aggarwal Consultant of: Mallinckrodt, Octapharma, CSL Behring, Bristol Myers-Squibb, EMD Serono, Kezar, Pfizer, AstraZeneca, Alexion, Argenx, Boehringer Ingelheim (BI), Corbus, Janssen, Kyverna, Roivant, Merck, Galapagos, Actigraph, Abbvie, Scipher, Horizontal Therapeutics, Teva, Biogen, Beigene, ANI Pharmaceutical, Nuvig, Capella, CabalettaBio, Grant/research support from: Bristol Myers-Squibb, Pfizer, Mallinckrodt, Janssen, Q32, EMD Serono, Boehringer Ingelheim, Latika Gupta: None declared.

Impact of Teachers' Support Strategies on Students' Engagement of Online College English Class Experiences

During the covid-19 pandemic, students' online learning quality is imbued with teachers' support strategies while students' learning engagement is another great indicator underlies their learning experiences. Through a questionnaire survey of 500 freshmen who have had their college English class online in 2022 fall, an investigation using exploratory factor analysis, Pearson correlation analysis, stepwise regression analysis and parallel mediator model reveals the impact of teachers' support strategies (the six dimensions of challenge, authentic context, curiosity, autonomy, recognition and feedback) on the learners' online college English learning engagement (the four dimensions of cognitive engagement, behavioral engagement, emotional engagement, social engagement), thus particular concern is also given to the correlation with students' online learning experiences. It was found that even under diversified and comprehensive guiding strategies from teachers, university students' online college English learning engagement is at the medium level, among which the cognitive engagement should be devoted more. The experimental data also shows that teachers' support strategies have significant influence on learners' engagement, especially teachers' feedback and challenge setting will stimulate students to involve more in their study. In addition, both teachers' support strategies and students' learning engagement involves significant reflection of learning experiences accordingly. Based on this learning concept, related proposals see different degrees of prominence reflected in online instructional design, teachers' and students' feedback literacy, and technology-enabled innovative teaching practice are put forward, in order to effectively play the role of teacher scaffolding, learning experiences enrichment and students' engagement enhancement of online English learning. © 2023 IEEE.

Research on the Relationship between Teacher Expectations and College Students' Online English Learning Engagement

As a critical influencing factor of learning engagement, teacher expectation plays a vital role in ensuring the quality of online teaching under COVID-19. This paper investigates the relationship between teacher expectations (three dimensions of teacher support, teaching interaction, and academic feedback) on students' online English learning engagement (three dimensions of cognitive engagement, behavioral engagement, and emotional engagement) in e-learning through a questionnaire survey of 513 college students. Pearson correlation analysis and multiple regression analysis were applied as research methods. The results manifest that college students' online English learning engagement was above average, but emotional engagement needs improvement. In addition, teacher expectations of teaching interaction positively and significantly predict English e-learning engagement. Based on this, the article puts forward suggestions on the future of online teaching from the aspects of online teaching design, feedback quality of teachers and students, innovative teaching practice of technology empowerment to effectively play the role of teachers as scaffolding and improve the effectiveness of online English teaching. © 2023 IEEE.

Stress reactions and health problems in hospital personnel during the COVID-19 pandemic-A longitudinal, multicenter study

Background: The overarching aim of the study was to (1) investigate how working with COVID-19 patients has impacted work environment, and (2) to identify how factors in the work environment impact adverse health outcomes among hospital personnel (HP), throughout the four waves of the pandemic. Material(s) and Method(s): In a web-based survey altogether 2472 HP participated from four large university hospitals in Norway, whereof N = 680 in April-June 2020 (T1), N = 1073 in December-January 2020/2021 (T2), N = 818 in April-May 2021 (T3), and N = 972 in December 2021-February 2022 (T4). At each time point participants reported on pandemic related work tasks, work environment and adverse health outcomes. Somatic symptoms, psychological distress, posttraumatic stress symptoms and burnout served as outcomes of multivariable linear regression models. The percentage of responders involved in ICU treatment of COVID-19 patients varied between 21% and 40% from T1-T4. Result(s): Reported stressors altered in strength during the 4 waves. Preliminary results indicate that exposure to patients with COVID-19 was associated with more frequent experience of work environmental factors. Compared to colleagues not working with patients with COVID-19 HP reported challenges related to professional competency and training, predictability in teams and work environment, manageable workload, adequate help and support for work stress management, user-friendliness of Personal Protection Equipment and infection protection safety. Furthermore, these environmental factors were associated with symptoms of psychological unhealth on at least one timepoint. Conclusion(s): The results may help guide organizational efforts to maintain professional competency and to reduce stress more efficiently among hospital personnel at different stages in response to long-term crises.

Workplace violence and associations with health outcomes in public health administration agencies in Taiwan: a survey during the COVID-19 pandemic

Objectives: To investigate the prevalence of workplace violence in public health administration agencies and its effects on health outcomes. Methods: A survey was conducted in March 2022. Staff who had been working for at least one year in the Ministry of Health and Welfare or its subordinate agencies, the Department of Health, or in public health centers were recruited. Data were collected anonymously with a structured, online questionnaire. A total of 492 valid questionnaires were collected. Results: A total of 48.17% participants reported having experienced workplace violence (physical, psychological, verbal, or sexual). The most common type of violence was verbal (43.50%), followed by psychological (31.71%). Supervisors were the primary perpetrators of verbal and psychological violence, followed by clients and colleagues. Staff reported long working hours and high levels of psychological and physical stress. Furthermore, 22.97% of workers reported poor self-rated health, 60.57% had personal burnout levels higher than 50, and 63.41% reported poor mental health. Regression analyses showed that low workplace justice was most strongly associated with internal verbal and psychological violence, whereas routine work requiring interaction with the public was most strongly associated with external verbal violence. Staff who had experienced workplace violence in the past year had significantly higher risks of poor self-rated health, mental health, and personal burnout, and poor health was more strongly associated with workplace violence that originated inside the organization than with workplace violence that originated from outside the organization. Conclusions: This survey was conducted on-line anonymously, so the representativeness of our findings might be limited. However, heavy workloads and workplace violence in public health administration agencies during the COVID-19 pandemic are important issues deserving urgent attention. (Taiwan J Public Health. 2023;42(1) :42-61)

The Application of PageRank model in urban epidemic prevention and control

The ongoing COVID-19 epidemic has had a great impact on social activities and the economy. The usage technical analysis tools to provide a more accurate and efficient reference for epidemic control measures is of great significance. This paper analyzes the characteristics and deficiencies of the existing technical methods, such as regression model, simulation calculation, differential equation and so on. By analyzing past outbreak cases and comparing the epidemic prevention measures of different cities, we discuss the importance of early and timely prevention in controlling the epidemic, and the importance of analyzing and formulating plans in advance. We then make the key observation that the spread of the virus is related to the topology of the urban network. This paper further proposes an epidemic analysis model of the optimized PageRank model, and gives a ranking algorithm for virus transmission risk levels based on road nodes, forming a visual risk warning level map, and applies the algorithm to the epidemic analysis of Yuegezhuang area in Beijing. Finally, more in-depth research directions and suggestions for prevention and control measures are put forward. © 2023 SPIE.

Predicting Student's Satisfaction towards Hybrid Learning in Informatics

This paper used regression and moderation approaches to evaluate the student's satisfaction with informatics towards the hybrid learning in their study. Multiple Linear Regression (MLR) identified student satisfaction based on hybrid learning difficulty and benefit ($p < 0.001$). Linear Regression (LR) found hybrid learning benefits impacted the student's satis-faction significantly $(p < 0.001$). Student's $t$-test also revealed that Overall Satisfaction (OS) significantly affected hybrid learning's satisfaction ($p < 0.001$). Analysis of Co-variants (ANCOVA) also proved that hybrid learning's benefit ($p < 0.001$) and OS ($p < 0.05$) significantly influenced student satisfaction. The paper also proved that hybrid learning's benefits positively correlate with student satisfaction (0.596). The slopes of 'Yes' and 'No' are substantially different from one another when the probability value of 0.22 $(p > 0.05$). Hence, no moderator (OS) affects the relationship's strength between the benefit and satisfaction of hybrid learning. The paper also revealed that hybrid learning's difficulty has a negative correlation (-.18), and the benefit of hybrid learning is positively associated with student satisfaction (.66). Implementing a hybrid learning mode during Covid-19 periods significantly impacted student satisfaction and the decision taken by the administration was also meaningful. © 2023 IEEE.

An Empirical Analysis of Efficiency in Accommodation Industry in Australian Tourism Regions

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Using the "omnicell" automatic drug dispensing cabinet to monitor sedative drugs in a critical care unit

Introduction: Automatic drug dispensers are now widely used in critical care.1-2 They can provide information about dispensed drugs. Good practice in sedation restricts the use of sedatives and titrates doses to defined responses.3-4 Objectives: To extract drug dispenser issuing records for sedatives and link these to patient records to evaluate sedative use. Method(s): in October 2019, we introduced two Omnicell XT automated dispensing cabinets (Omnicell inc. CA, USA) into a 42 bedded general/neurological unit. ICNARC (Intensive care national audit and research centre) and CCMDS (Critical care minimum data set) data was collected using the Ward Watcher program. Dispenser issuing records for alfentanil, propofol and midazolam were obtained as Excel files for 13 months from January 2020. Output time stamps were converted to dates and times. Outputs were linked to outputs of the ICNARC and CCMDS records for the patients that the drugs were issued to. All the outputs had patients identified by their unique hospital numbers. These were used in Excel "power queries" to produce a spread sheet with a single row per patient. Multiple admissions used the first diagnosis, the final outcome and the total length of stay. The total dose of sedatives was calculated from ampoule dose and number. The duration of treatment was calculated from the first and last issues of the drug. ICNARC codes were used to identify the primary system in the admission diagnostic code and those patients admitted for COVID-19. Variables were compared using Chi Squared, Mann-Whitney U and Kruskal Wallis Tests. The significance of associations was established using Spearman's Rho. Linear regression was used to define relationships more clearly. Result(s): Table one summarises the patient characteristics with respect to all admissions during the study period and for patients who had had the studied drugs issued to them. Midazolam was used in fewer patients, they were more likely to be male, heavier (p>0001) and to die than patients receiving Propofol or Alfentanil (p>0.001). With respect to diagnostic groups, all the sedatives, particularly Midazolam (p<0.001), were more likely to be used in patients with COVID-19. The relationship between the dose of sedative drugs and patient age and weight was explored using the dose per advanced respiratory day. All three drugs had a significant but weak negative relationship with age, lower doses being given to older people (Propofol r2 = 0.02, p=0.01. Alfentanil r2 = 0.04, p=0.00. Midazolam r2 = 0.07, p=0.00.). There was also a weak but significant relationship between increasing dose of Propofol with patient weight (r2 = 0.02, p=0.01), but there was no relation between weight and doses of the other drugs. Conclusion(s): Information from automatic drug dispensers can be interpreted and combined with other datasets to produce clinically relevant information. The limited weak relationships between drug dose and age and weight suggests that sedative drugs could have been better titrated to response.

Income Recovery of Thai Workers From the COVID-19 Pandemic

This paper examines how COVID-19 and the resultant lockdown affected Thai workers and how their income has recovered as of the end of 2020. We conducted three phases of telephone surveys to track the income dynamics of Thai workers during the months of May, August, and November 2020. The initial COVID-19 impact on Thai worker income was enormous and very broad. On average, Thai workers' income fell by 47.03%, and 69.7% suffered such a loss. Over the six months survey period, most Thai workers had just begun to stabilize their income, but only a few were actually able to recover. Quantile regression analysis revealed particular factors that influenced income recovery. For example, being a formal worker tended to help one's income to recover faster. Interestingly, COVID-19 assistance schemes from the government, although essential to those in need, had a negative impact on income recovery. On the other hand, the cheap loan policy seems to have been more effective as workers whose incomes were in the middle and the top quantiles experienced faster income recovery. © 2023 by De La Salle University.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

The Impact of Strategic Experiential Modules (SEMs) Dimension on Visitor's Revisit Intention: A Lesson from the Sunrise of Java, Indonesia: Acces la Success

Mangrove tourism is one of the tourist destinations offered by tourism managers that is currently gaining popularity and popularity among tourists. Keeping tourists coming back can be a very effective strategy for developing tourist destinations. This study employs Experiential Marketing as a strategy to increase tourist interest. Because research in the field of experiential marketing in nature tourism destinations such as mangrove tourism is still limited, the topics of this study are experiential marketing and visitor visit intention. The purpose of this study was to determine the impact of strategic experiential modules (SEMs) on visitor revisits intention. The research method used is quantitative with the variable dimensions of SEMs and visitor revisits intention, a sample of 93 tourists with a purposive sampling technique, and multiple linear regression analysis techniques. The results showed that the sense, act, and relate variables had a positive and significant impact on the visitor revisits intention, while the feel variable had a positive but not significant impact, and the think variable had a negative but not significant impact on the visitor revisits intention.

Smart Tourism using Attractive and Safe Travel Recommendation Technology

The tourism sector was among the most affected sector during the COVID-19 pandemic and has lost up to USD 5.87 billion potential revenue. Since many countries closed the borders, including Indonesia, by applying travel restrictions and thus tourists postponed their visits. Whereas vaccine distribution has shown good progress as the vaccination percentage in Jakarta and Bali has shown promising results since the majority of its population has been vaccinated, and it helps many industries, including tourism, recover. However, the pandemic might change tourist behavior. In addition, information about tourist destinations is spread poorly in various sources, and it psychologically affects tourists' decision to visit. Many works have been published to address this issue with the recommendation system. However, it does not provide geopolitical variables such as PPKM in Indonesia to ensure safeness for the tourist. Therefore, this research aims to enhance innovations in the tourism industry by considering the geopolitics factor into the system using Multiple Linear Regression. The result of this research demonstrates the effectiveness of geopolitics added variable on three different cities Jakarta, Java, and Bali. It can be implemented in a wide area in Indonesia. For further research, the proposed model can be used in a wide area in Indonesia and developed for a more comprehensive recommendation system. © 2022 IEEE.