关键词： Pedagogy method selection   Inclusivity   Post-COVID-19   Interval -valued fuzzy   Multi -criteria decision -making  

摘要： The advent of the COVID-19 pandemic has brought about unprecedented disruptions in the field of education, necessitating a reevaluation of pedagogical approaches in the post-COVID era. This research paper introduces a novel interval-valued fuzzy simple additive weighting (SAW), weighted product model (WPM), and weighted aggregates sum product assessment (WASPAS) multi-criteria decision-making (MCDM) framework to address the challenge of promoting inclusivity in education amidst the post-COVID era. Leveraging the uncertainty inherent in the postpandemic educational landscape, the proposed method offers a comprehensive pedagogy selection approach incorporating interval-valued fuzzy sets to account for imprecise and ambiguous data. By integrating the principles of inclusivity and diversity, the method evaluates various pedagogical approaches and their effectiveness in fostering an inclusive learning environment for diverse student populations. The study showcases the application of pedagogy selection in realworld educational scenarios, demonstrating its potential to inform policy decisions and enable educational institutions to adapt and cater to the evolving needs of learners in the aftermath of the COVID-19 pandemic.

关键词： COVID-19   Syndemic   Chronic diseases   Mental health   DISEASE   CARE  

摘要： Objectives: There is a need to consider COVID-19 a syndemic; which calls for a comprehensive approach to tackle the associated interconnected challenges. The objective of this study is to investigate the potential syndemic nature of COVID-19, with a specific focus on understanding how viral infection, mental health (such as anxiety and depression), and pre-existing comorbidities interact and influence each other. Study Design: Retrospective population-based cohort study. Methods: We conducted a population-based retrospective cohort study using linked health administrative data from the Institute for Clinical Evaluative Sciences, Ontario. The study included 2,863,423 Ontario residents from January 2020 to March 2021. We analysed healthcare services utilisation (physician visits, emergency visits, and hospitalisations) for chronic conditions among individuals with both COVID-19 and either anxiety or depression, to understand the syndemic impact of COVID-19 and mental health issues among Ontario population. Results: Multiple regression models were used to explore the study's objective. In the final adjusted regression model for the sample, it was found that the individuals who were COVID-19 positive and had either anxiety or depression were more likely to utilise health services for chronic conditions of interest during the pandemic than those who were COVID-19-negative with mental health issues (odds ratio [OR]:, 1.33; 95% confidence interval [CI]: 1.12-1.58). A higher risk of morbidity was observed among males (OR: 1.28; CI: 1.16-1.41), as well as in individuals with diverse ethnic backgrounds and low socioeconomic status. Conclusions: The impact of COVID-19 on mental health, particularly among vulnerable populations with chronic diseases, can be seen as a syndemic. This complex interaction emphasises the need for integrated public health strategies. (c) 2024 The Author(s). Published by Elsevier Ltd on behalf of The Royal Society for Public Health. This is an open ac

关键词： COVID-19   Economic impacts   Seafood   Fisheries   Expenditure   Spain  

摘要： This paper aims to develop an assessment of the COVID-19 impact on key maritime sectors (small-scale and large fisheries) in Spain, one of the most important maritime regions in the world. We synthesize multiple data sources from across the seafood supply chain to show the relative seafood sector responses and variables of recovery during the pre-COVID-19 period (2015-2019) and during the pandemic (2020). Our results show that the reported landings made in 2020 by the different Spanish fleets (786,900 t) represented an 11 % reduction compared to the annual average landed between 2015 and 2019. On average, the fishing effort of the small-scale fishing fleets suffered a higher decline (16 %) related to the large-scale fleet (5 %), but the large-scale fleet experienced a higher reduction (25 %) in landing value between the pre-COVID-19 and pandemic. Although the decrease in catches affected almost all the Spanish-studied regions, the reductions were especially relevant in the Basque Country (-14 %) and Galicia (-11 %), since they are the main fishing regions responsible for 76 % of Spanish landings. The volume and value of imports and exports of fresh, frozen, and canned products in 2020 did not change (+/- 5 %) compared to the previous period (2015-2019) but seafood trade was affected by country of origin and destination and by Autonomous Community. Overall, our study highlights that the fisheries sector in Spain was also able to implement a range of adaptive strategies, such as diversification of species portfolios or distribution of seafood door-to-door which appeared swiftly to compensate for the vanishing restaurant markets.

关键词： Long COVID disease   IRS   Neurotoxicity   Inflammation   Immune activation   Cell-mediated immunity   ENCEPHALOMYELITIS/CHRONIC FATIGUE SYNDROME   CELL-MEDIATED-IMMUNITY   INFLAMMATION   DEPRESSION   SEQUELAE   SOMATIZATION   PATHWAYS   STRESS   BRAIN  

摘要： Background: Multiple studies have shown that Long COVID (LC) disease is associated with heightened immune activation, as evidenced by elevated levels of inflammatory mediators. However, there is no comprehensive metaanalysis focusing on activation of the immune inflammatory response system (IRS) and the compensatory immunoregulatory system (CIRS) along with other immune phenotypes in LC patients. Objectives: This meta-analysis is designed to explore the IRS and CIRS profiles in LC patients, the individual cytokines, chemokines, growth factors, along with C-reactive protein (CRP) and immune-associated neurotoxicity. Methods: To gather relevant studies for our research, we conducted a thorough search using databases such as PubMed, Google Scholar, and SciFinder, covering all available literature up to July 5th, 2024. Results: The current meta-analysis encompassed 103 studies that examined multiple immune profiles, C-reactive protein, and 58 cytokines/chemokines/growth factors in 5502 LC patients versus 5962 normal controls (NC). LC patients showed significant increases in IRS/CIRS ratio (standardized mean difference (SMD: 0.156, confidence interval (CI): 0.062;0.250), IRS (SMD: 0.338, CI: 0.236;0.440), M1 macrophage (SMD: 0.371, CI: 0.263;0.480), T helper (Th)1 (SMD: 0.316, CI: 0.185;0.446), Th17 (SMD: 0.439, CI: 0.302;0.577) and immune-associated neurotoxicity (SMD: 0.384, CI: 0.271;0.497). In addition, CRP and 21 different cytokines displayed significantly elevated levels in LC patients compared to NC. Conclusion: LC disease is characterized by IRS activation and increased immune-associated neurotoxicity.

关键词： Mechanical ventilation   COVID-19 Pandemic   Healthcare resource allocation   Patient mortality   Critical care   Healthcare crisis response   CANCER  

摘要： Background: The COVID-19 pandemic has presented unprecedented challenges for healthcare systems globally, impacting critical care resources and patient outcomes. Understanding its multifaceted effects is crucial for future crisis response. Objective: Analyze the repercussions of the COVID-19 pandemic on mechanical ventilation cases and mortality among non-SARS-CoV-2 patients. Methods: A nationwide database encompassing all patients receiving mechanical ventilation in Spain was used to compare the number of cases and clinical outcomes during COVID-19 (March 2020 - December 2021) to prepandemic cases (May 2018 - February 2020). Univariate and multivariate analyses were employed. Results: COVID-19 significantly reduced access to ventilation for non-COVID-19 patients. A 16 % decrease (12,099 fewer patients) was observed during the pandemic compared to pre-pandemic times. This reduction affected all analyzed conditions except self-inflicted injuries, coinciding with a rise in overall mortality risk (34.5% vs 35.6 %, OR 1.09, 95 %CI 1.06-1.12). The increased mortality was consistent across diverse admission types, including cancer (37.1% vs. 41.5 %, OR 1.18, 95 %CI 1.09-1.29), hemorrhagic strokes (55.4% vs. 56.6 %, OR 1.07, 95 %CI 1.02-1.20), acute myocardial infarction (35.6% vs. 38 %, OR 1.11, 95 %CI 1.01-1.21), nonSARS-CoV-2 pneumonia (44.5% vs. 45.8 %, OR 1.12, 95 %CI 1.02-1.24), septic shock (54.7% vs. 56.3 %, OR 1.10, 95 %CI 1.06-1.15), and prolonged ventilation (>= 96 h) (37% vs. 38.2 %, OR 1.10, 95 %CI 1.06-1.10). Conclusions: The findings underscore the profound impact of the COVID-19 pandemic on critical care utilization and patient outcomes among non-SARS-CoV-2 patients. As healthcare systems strive to mitigate future crises, these insights emphasize adaptable strategies for equitable access to life-saving treatments.

关键词： Tourism economic impact   COVID-19   Dynamic stochastic general equilibrium   Macroeconomic modeling   ECONOMIC-GROWTH   PRODUCTIVITY   MONETARY   POLICY  

摘要： This paper develops a Dynamic Stochastic General Equilibrium model to gauge on the detrimental effects of COVID-19 on the tourism sector and consequently on the macroeconomy of Mauritius. Findings demonstrate COVID-19-induced tourism export shock triggering subdued exports, imports, investments and tax revenues while government debt, inflation rate, transfer income experienced bullish momentum. Overall, results from the model bode well with the real macroeconomic conditions which prevailed post the pandemic. Policywise, findings advocate the development of a new sector to diversify the exporting arm of the Mauritian economy. Our model can be used not only by local authorities but also by foreign economies which exhibit similar features to those of the Mauritian economy in view of shaping out optimal policies.

关键词： Multivariate time series   Dendritic computation   Neural network   Forecasting   COVID-19  

摘要： A novel coronavirus discovered in late 2019 (COVID-19) quickly spread into a global epidemic and, thankfully, was brought under control by 2022. Because of the virus's unknown mutations and the vaccine's waning potency, forecasting is still essential for resurgence prevention and medical resource management. Computational efficiency and long-term accuracy are two bottlenecks for national-level forecasting. This study develops a novel multivariate time series forecasting model, the densely connected highly flexible dendritic neuron model (DFDNM) to predict daily and weekly positive COVID-19 cases. DFDNM's high flexibility mechanism improves its capacity to deal with nonlinear challenges. The dense introduction of shortcut connections alleviates the vanishing and exploding gradient problems, encourages feature reuse, and improves feature extraction. To deal with the rapidly growing parameters, an improved variation of the adaptive moment estimation (AdamW) algorithm is employed as the learning algorithm for the DFDNM because of its strong optimization ability. The experimental results and statistical analysis conducted across three Japanese prefectures confirm the efficacy and feasibility of the DFDNM while outperforming various state-of-the-art machine learning models. To the best of our knowledge, the proposed DFDNM is the first to restructure the dendritic neuron model's neural architecture, demonstrating promising use in multivariate time series prediction. Because of its optimal performance, the DFDNM may serve as an important reference for national and regional government decision- makers aiming to optimize pandemic prevention and medical resource management. We also verify that DFDMN is efficiently applicable not only to COVID-19 transmission prediction, but also to more general multivariate prediction tasks. It leads us to believe that it might be applied as a promising prediction model in other fields.

关键词： COVID-19   Computer tomography   CNN   Diagnosis   Image selection   Voting system  

摘要： COVID-19 has spiked worldwide, having multiple outbreaks even with the production of vaccines. Imaging exams, such as Computer Tomography (CT) and X-ray, are recommended by the World Health Organization after performing RT-PCR tests to enhance COVID-19 diagnosis for serious cases. This work proposes a deep learning methodology to evaluate whether a patient presents COVID-19-related findings in CT images as an auxiliary diagnostic tool. As a CT exam produces many images related to a patient, some are irrelevant for COVID-19 diagnosis (e.g., closed lungs), using the raw information may hinder the model. Hence, we provide a CT scan image selection algorithm to filter the most informative images with two versions: (a) non-sequential, and (b) sequential. Then, online data augmentation is applied before feeding these images to a Convolutional Neural Network (CNN). Moreover, we evaluate the performance of the model for both versions of the CT selection algorithm in different approaches: (i) 'per-image', (ii) 'per-patient majority voting', and (iii) 'perpatient conservative voting (30%)'. We applied the proposed methodology in a Brazilian university hospital, a reference for COVID-19 treatment. For the test set, approaches (a-i), (a-ii), and (a-iii) reached an accuracy of 84.6%, 70%, and 70%, respectively, while approaches (b-i), (b-ii), and (b-iii) reached 90.9%, 80%, and 80%, respectively. Hence, we consider the proposed sequential version the most suitable image selection algorithm for the analyzed data set and useful for supporting decisions in COVID-19 diagnosis.

关键词： Meta-synthesis   COVID-19   Pandemic   Midwives   Midwifery   Experience   PERSPECTIVES   MIDWIFERY  

摘要： Problem: During the COVID-19 pandemic, midwives faced emotional and physical risks while on the frontlines providing care. Background: To maintain a healthy midwifery workforce, it is necessary to understand midwives' pandemic challenges and successes, including how they personally and professionally faced changes to care provision. Aim: The aim of this meta-synthesis was to understand the experiences of midwives working during the COVID19 pandemic. Methods: Noblit and Hare's (1988) approach to synthesising qualitative research studies was followed. Fifteen qualitative research reports were identified using PRISMA guidelines, producing a sample of 588 midwives from 12 countries. Findings: The synthesis revealed three overarching themes: (1) Turmoil and confusion: a spectrum of emotions due to ever-changing protocols, (2) COVID-19 stripped the "being with" out of midwifery care, and (3) Finding our way: midwifery resilience and growth. Analysed together, these three themes contribute to understanding the experiences of midwives working during the COVID-19 pandemic. Discussion: Midwives experienced fear, stress, and anxiety. They felt they couldn't physically be with women during the pandemic. They were frustrated by being left out of institutional decision-making regarding COVID-19 protocols that impacted the women they served. Professional growth as a midwife, and personal resilience were ultimately realised. Conclusions: The COVID-19 pandemic disrupted the routine ways in which midwives provide care in all settings. Understanding the complete experience of midwives during the pandemic allows for midwifery organizations to be aware of their members' needs, as well as for institutions to ensure supports are in place for midwives in the event of future pandemics.

关键词： Adolescent pregnancy   COVID-19   Qualitative studies   Vulnerable population   Mental health   HEALTH   PREGNANCY   OUTCOMES   WOMEN  

摘要： Objectives: To understand how the perinatal care experiences among adolescents were impacted during the early years of the COVID-19 pandemic. Study design: Integrative review. Methods: We performed a search in the literature focusing on qualitative studies regarding the perceptions of pregnant adolescents during the pandemic (2020-2023). We described the most frequent thematic axes observed in the included studies, and we synthesized the results. Results: The selection involved 69 articles, resulting in 9 studies from 7 countries (Indonesia, Kenya, the United Kingdom, South Africa, the United States, Malawi, and India). The studies highlight six main thematic axes: 1. Economic impact: the pandemic adversely influenced personal and family conditions, leading to early marriages and violence and exacerbating pre-existing economic inequalities. 2. Mental health: increased anxiety, depression, isolation, and fear among pregnant adolescents, with a focus on concerns related to contamination and perinatal uncertainties. 3. Compromised perinatal care: restrictions during prenatal and delivery care and lack of emotional and professional support lead to negative impacts on care for pregnant adolescents. 4. Breakdown of family Support network: school closures, family losses, and increased domestic violence affected family dynamics, influencing the occurrence of early pregnancies. 5. Impact on sexual education: school closures hindered access to contraceptives, contributing to unplanned pregnancies. 6. Vaccine hesitancy: misinformation led to hesitancy among pregnant women, highlighting the need for better communication to increase confidence in vaccination. Conclusions: The review emphasizes the wide geographic variety of the studies and highlights the interconnection between economic, social, and mental health factors. The pandemic intensified preexisting challenges, underscoring the importance of comprehensive support for pregnant adolescents, including emotional and psychol