Ongoing Pandemic Research Articles

Challenges of remote working, perceived peer support, mental health and well-being of WIL students

PurposeThis study explored the relationship between perceived peer support, mental health and the well-being of students enrolled in work-integrated learning (WIL) at a Canadian institute of higher education, who were completing remote work experiences.Design/methodology/approachAn online survey and virtual semi-structured interviews were used to collect data. The online survey captured demographic information as well as measures of perceived peer support, loneliness, positive mental health (PMH) and stressors associated with the on-going pandemic. The interviews captured narratives regarding peer support, attitudes surrounding remote work, mental health and well-being.FindingsWIL students completing remote work terms experienced only moderate levels of peer support, moderate loneliness, below-average PMH and all the stressors associated with the on-going pandemic. Data also revealed that completing a remote work term negatively impacted work communications, opportunities to build connections with colleagues and overall motivation. On the other hand, WIL students appreciated the flexibility and comfort of working from home, as well as reduced work-related expenses.Originality/valueWith remote work experiences increasing globally and now a reality for many WIL students, the potential negative effects emphasize the importance of providing social and mental health support and resources, especially during stressful times.

The Mystery Arts Box Project: a qualitative exploration of the experiences, benefits, and challenges of participating in a remotely delivered art and craft project for British veterans with visual impairment

IntroductionThe clinical application of the arts among military personnel and veterans has been well documented, particularly in relation to service-related mental health difficulties. However, the impacts of engaging recreationally with...

Construction of China's food security evaluation index system and spatiotemporal evolution.

Food security is a vital material foundation for a nation's development and has been a topic of significant concern on the international stage in recent years. With a population exceeding 1.4 billion, China is not only a major producer but also a substantial consumer of food. Ensuring food security in China is not only a top priority for its socio-economic development but also a driving force in maintaining the stability of the global food supply chain and reducing the number of hungry people worldwide. However, a lack of comprehensive research into the Chinese food security system remains. This study addresses this gap by constructing a comprehensive evaluation framework encompassing four dimensions: food supply, accessibility, production stability, and sustainability. Utilizing the Moran's Index and generating LISA (Local Indicators of Spatial Association) maps, we analyze the spatial correlations of food security. The Dagum Gini coefficient and kernel density estimation are applied to assess heterogeneity and spatial disparities. Furthermore, this research employs the Exponential Smoothing (ETS) model to forecast food security trends. The findings reveal that the overall composite food security score exhibited fluctuations, initially increasing and reaching its peak of 0.407 in 2003, followed by a subsequent sharp decline after 2019. Spatially, food security exhibits correlations, with the Huang-Huai-Hai Plain and Northeast regions consistently showing high-high clustering. In contrast, the Western and Southern regions exhibit low-low clustering at specific periods. The Dagum Gini coefficient indicates that overall food security disparities are relatively small. However, these disparities have gradually expanded in recent years, with inter-group differences becoming predominant after 2005. As indicated by the kernel density estimation, the dynamic distribution of food security initially widens and then narrows, suggesting a shift from dispersed to concentrated data distribution. This phenomenon is accompanied by polarization and convergence trends, particularly evident after 2015. According to the ETS model, the study forecasts a substantial risk of declining food security in China over the next decade, largely influenced by the ongoing pandemic. In conclusion, this research provides a comprehensive assessment of the changing status of food security in China. It offers early warnings through predictive analysis, addressing the existing research gaps in the field of food security.

Efficacy of memory protocols in 9- to 89-year-olds’ memory for prior contacts

Memory for prior contacts has several important applied implications, including contact tracing (for communicable diseases). Incomplete episodic memory reports, which occur across the developmental lifespan but are particularly relevant for children and older adults, may hamper such efforts. Prior research has shown that cognitively informed memory techniques may bolster recall of contacts in adults, but that work has not addressed the developmental efficacy of these techniques. Here we evaluated the effectiveness of such techniques for familiar and unfamiliar contacts within a sample of 9- to 89-year-olds in the context of an ongoing pandemic. The tested memory techniques bolstered recall across the lifespan, irrespective of whether the interview was conducted live with an interviewer or via a self-led interview. Children, emerging adults, and adults did not reveal any differences in memory productivity, however, older adults recalled fewer contacts. Implications for theory and application are discussed.

The influence of COVID-19 on the health sector

Healthcare, as a fundamental component of human well-being, plays a pivotal role in shaping overall quality of life. This research investigates the extensive ramifications of the COVID-19 epidemic on the healthcare sector, encompassing the challenges and opportunities that have emerged in its wake. This study investigates the implications for hospital infrastructure through a comprehensive analysis of pertinent literature. The findings reveal significant deficiencies in essential resources such as ventilators and personal protective equipment (PPE). The evident manifestation of the psychological burden experienced by healthcare personnel highlights the imperative of allocating additional resources to support them. The advent of the pandemic has given rise to telemedicine and other digital alternatives, leading to a significant transformation in the field of healthcare delivery. Lockdowns, mask mandates, and other public health interventions are crucial in mitigating the transmission of the pathogen. Despite facing significant challenges, the ongoing pandemic has accelerated advancements in the field of medicine. This study contributes to the existing body of knowledge regarding effective strategies for addressing future pandemics and underscores the imperative of establishing a healthcare system that is adaptable and responsive. In the era following the COVID-19 pandemic, it is imperative to proactively tackle challenges and explore untapped opportunities in the healthcare sector by continuous research and assertive endeavors.

Investigating Neonatal Multisystem Inflammatory Syndrome Associated with COVID-19 in the Neonatal Intensive Care Unit: A Case Series

Background: The COVID-19 pandemic, caused by the novel SARS-CoV-2 virus, has presented significant challenges to global healthcare systems, affecting individuals of all ages, including neonates. Recent studies have identified a condition similar to Multisystem Inflammatory Syndrome in Children (MIS-C) occurring in neonates, termed MIS-N (Multisystem Inflammatory Syndrome in Neonates), linked to maternal infection with SARS-CoV-2. Understanding MIS-N's clinical manifestations, laboratory findings, and treatment outcomes is crucial for improving neonatal care during the ongoing pandemic. Objective: The objective of this study was to explore the clinical characteristics, laboratory diagnostics, and treatment outcomes of neonates diagnosed with MIS-N, aiming to contribute to better diagnostic and management approaches for this vulnerable population. Methods: This retrospective case series analyzed neonates admitted with suspected MIS-N to the Children's Medical Center in Tehran, Iran, from March to September 2020. Inclusion criteria encompassed neonates showing clinical signs suggestive of MIS-N, elevated inflammatory markers, and positive for SARS-CoV-2 antibodies. Key data points included demographic information, clinical presentation, laboratory results (including complete blood count, inflammatory markers, and specific COVID-19 serology), treatment modalities, and patient outcomes. The effectiveness of interventions was evaluated based on symptom resolution and normalization of lab values. Results: Two neonates were identified with MIS-N. Case I involved a 39-week-old male presenting with diarrhea, dehydration, fever, and a rash, with initial inflammatory markers showing a white blood count (WBC) of 23,000 cells/μL, C-reactive protein (CRP) of 93 mg/L, and serum IgG of 10 g/L. Following treatment with corticosteroids, the patient's condition stabilized, with a decrease in WBC to 18,000 cells/μL and CRP to 34 mg/L. Case II, a 38-week-old female, exhibited a cough, rashes, with WBC of 17,000 cells/μL, CRP of 2 mg/L, and serum IgG of 40 g/L. A single dose of hydrocortisone led to symptom resolution. Both cases had no significant adverse outcomes at a three-month follow-up. Conclusion: Our findings illuminate the clinical and laboratory characteristics of MIS-N in neonates, emphasizing the role of maternal SARS-CoV-2 infection in its etiology. The successful resolution of symptoms with corticosteroid treatment highlights the potential efficacy of this intervention in managing MIS-N. These insights contribute to the growing understanding of MIS-N and underscore the need for ongoing research to define optimal diagnostic criteria and treatment protocols.

Deciphering the omicron variant: integrated omics analysis reveals critical biomarkers and pathophysiological pathways

BackgroundThe rapid emergence and global dissemination of the Omicron variant of SARS-CoV-2 have posed formidable challenges in public health. This scenario underscores the urgent need for an enhanced understanding of Omicron's pathophysiological mechanisms to guide clinical management and shape public health strategies. Our study is aimed at deciphering the intricate molecular mechanisms underlying Omicron infections, particularly focusing on the identification of specific biomarkers.MethodsThis investigation employed a robust and systematic approach, initially encompassing 15 Omicron-infected patients and an equal number of healthy controls, followed by a validation cohort of 20 individuals per group. The study's methodological framework included a comprehensive multi-omics analysis that integrated proteomics and metabolomics, augmented by extensive bioinformatics. Proteomic exploration was conducted via an advanced Ultra-High-Performance Liquid Chromatography (UHPLC) system linked with mass spectrometry. Concurrently, metabolomic profiling was executed using an Ultra-Performance Liquid Chromatography (UPLC) system. The bioinformatics component, fundamental to this research, entailed an exhaustive analysis of protein–protein interactions, pathway enrichment, and metabolic network dynamics, utilizing state-of-the-art tools such as the STRING database and Cytoscape software, ensuring a holistic interpretation of the data.ResultsOur proteomic inquiry identified eight notably dysregulated proteins (THBS1, ACTN1, ACTC1, POTEF, ACTB, TPM4, VCL, ICAM1) in individuals infected with the Omicron variant. These proteins play critical roles in essential physiological processes, especially within the coagulation cascade and hemostatic mechanisms, suggesting their significant involvement in the pathogenesis of Omicron infection. Complementing these proteomic insights, metabolomic analysis discerned 146 differentially expressed metabolites, intricately associated with pivotal metabolic pathways such as tryptophan metabolism, retinol metabolism, and steroid hormone biosynthesis. This comprehensive metabolic profiling sheds light on the systemic implications of Omicron infection, underscoring profound alterations in metabolic equilibrium.ConclusionsThis study substantially enriches our comprehension of the physiological ramifications induced by the Omicron variant, with a particular emphasis on the pivotal roles of coagulation and platelet pathways in disease pathogenesis. The discovery of these specific biomarkers illuminates their potential as critical targets for diagnostic and therapeutic strategies, providing invaluable insights for the development of tailored treatments and enhancing patient care in the dynamic context of the ongoing pandemic.

Therapeutic Effects of a Novel Aptamer on Coronaviral Infection-Induced Lung Injury and Systemic Inflammatory Responses.

Coronaviral infection-induced acute lung injury has become a major threat to public health, especially through the ongoing pandemic of COVID-19. Apta-1 is a newly discovered Aptamer that has anti-inflammatory effects on systemic septic responses. The therapeutic effects of Apta-1 on coronaviral infection-induced acute lung injury and systemic responses were evaluated in the present study. Female A/J mice (at 12-14 weeks of age) were challenged with murine hepatitis virus 1 (MHV-1), a coronavirus, at 5000 PFU intranasally, followed by Apta-1 intravenously administered (100 mg/kg, twice) 1.5 h or 2 days after viral delivery. Animals were sacrificed at Day 2 or Day 4. Lung tissues were examined with H&E, immunohistochemistry staining, and western blotting. RT-qPCR was used for cytokine gene expression. Serum and plasma were collected for laboratory assessments. Apta-1 treatment reduced viral titers, prevented MHV-1-induced reduction of circulating blood volume and hemolysis, reduced alveolar space hemorrhage, and protease-activated receptor 1 (PAR-1) cleavage. Apta-1 treatment also significantly reduced chemokine (MKC, MCP-1, and RANTES) levels, as well as AST, ALT, total bilirubin, and reduced unconjugated bilirubin levels in the serum. Apta-1 showed therapeutic benefits in coronaviral infection-induced hemorrhage and PAR-1 cleavage in the lung. It also has anti-inflammatory effects systemically.

Automated machine learning for the identification of asymptomatic COVID-19 carriers based on chest CT images.

Asymptomatic COVID-19 carriers with normal chest computed tomography (CT) scans have perpetuated the ongoing pandemic of this disease. This retrospective study aimed to use automated machine learning (AutoML) to develop a prediction model based on CT characteristics for the identification of asymptomatic carriers. Asymptomatic carriers were from Yangzhou Third People's Hospital from August 1st, 2020, to March 31st, 2021, and the control group included a healthy population from a nonepizootic area with two negative RT‒PCR results within 48h. All CT images were preprocessed using MATLAB. Model development and validation were conducted in R with the H2O package. The models were built based on six algorithms, e.g., random forest and deep neural network (DNN), and a training set (n = 691). The models were improved by automatically adjusting hyperparameters for an internal validation set (n = 306). The performance of the obtained models was evaluated based on a dataset from Suzhou (n = 178) using the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and F1 score. A total of 1,175 images were preprocessed with high stability. Six models were developed, and the performance of the DNN model ranked first, with an AUC value of 0.898 for the test set. The sensitivity, specificity, PPV, NPV, F1 score and accuracy of the DNN model were 0.820, 0.854, 0.849, 0.826, 0.834 and 0.837, respectively. A plot of a local interpretable model-agnostic explanation demonstrated how different variables worked in identifying asymptomatic carriers. Our study demonstrates that AutoML models based on CT images can be used to identify asymptomatic carriers. The most promising model for clinical implementation is the DNN-algorithm-based model.

The Adoption of Digital Technologies in Supply Chains in the Manufacturing Area

The Ongoing COVID pandemic has hampered economic activity; Food is a basic need that must be fulfilled and cannot be postponed. During the current pandemic, business people need to pay attention to the situation and conditions, especially the limitation of economic activity in several regions. However, Digital technologies have been extensively studied in academic research and industry. Moreover, little is known about adopting digital technologies in manufacturing firms at a supply chain level. This paper aims to understand why and how manufacturing firms adopt digital technologies and the impact of the adoption on supply chains. This research includes the design of a Web-based Supply Chain Management application to facilitate inventory management, starting from the process of ordering goods from suppliers and procurement of goods to managing goods to customers. Digital technology in the supply chain in the manufacturing area helps companies to store goods management data, raw materials, inventories, deliveries, and daily deposits, and others that are still using manual systems or not yet computerized to maximize performance and also profits for the company, suppliers, including end customers. The study identifies that technological intelligence and supply chain cooperation are critical factors and proposes two-dimensional levels of adopting digital technologies according to their low-to-high degrees.

Neutralizing antibodies after the third COVID-19 vaccination in healthcare workers with or without breakthrough infection

BackgroundVaccinations against the SARS-CoV-2 are still crucial in combating the ongoing pandemic that has caused more than 700 million infections and claimed almost 7 million lives in the past four years. Omicron (B.1.1.529) variants have incurred mutations that challenge the protection against infection and severe disease by the current vaccines, potentially compromising vaccination efforts.MethodsWe analyzed serum samples taken up to 9 months post third dose from 432 healthcare workers. Enzyme-linked immunosorbent assays (ELISA) and microneutralization tests (MNT) were used to assess the prevalence of vaccine-induced neutralizing antibodies against various SARS-CoV-2 Omicron variants.ResultsIn this serological analysis we show that SARS-CoV-2 vaccine combinations of BNT162b2, mRNA-1273, and ChAdOx1 mount SARS-CoV-2 binding and neutralizing antibodies with similar kinetics, but with differing neutralization capabilities. The most recent Omicron variants, BQ.1.1 and XBB.1.5, show a significant increase in the ability to escape vaccine and infection-induced antibody responses. Breakthrough infections in thrice vaccinated adults were seen in over 50% of the vaccinees, resulting in a stronger antibody response than without infection.ConclusionsDifferent three-dose vaccine combinations seem to induce considerable levels of neutralizing antibodies against most SARS-CoV-2 variants. However, the ability of the newer variants BQ1.1 and XBB 1.5 to escape vaccine-induced neutralizing antibody responses underlines the importance of updating vaccines as new variants emerge.

Inequitable Changes in School Connectedness During the Ongoing COVID-19 Pandemic in a Cohort of Canadian Adolescents.

We examined whether subgroups of adolescents experienced disparate changes in school connectedness-a robust predictor of multiple health outcomes-from before the COVID-19 pandemic to the first full school year following pandemic onset. We used 2 waves of prospective survey data from 7178 students attending 41 Canadian secondary schools that participated during the 2019-2020 (T1; pre-COVID-19 onset) and 2020-2021 (T2; ongoing pandemic) school years. Fixed effects analyses tested differences in school connectedness changes by gender, race, bullying victimization, socioeconomic position, and school learning mode. Relatively greater declines in school connectedness were reported by students that identified as females, were bullied, perceived their family to be less financially comfortable than their classmates, and attended schools in lower income areas. Marginally greater school connectedness declines resulted among students attending schools that were fully online at T2 than those at schools using a blended model. Results point to disparate school connectedness declines during the pandemic, which may exacerbate pre-existing health inequities by gender and socioeconomic position, and among bullied youth. Effective strategies to improve school climates for equity denied groups are critical for pandemic recovery and preparedness for future related events.

Survival analysis of the duration of rumors during the COVID-19 pandemic.

The emergence of the COVID-19 pandemic towards the end of 2019 triggered a relentless spread of online misinformation, which significantly impacted societal stability, public perception, and the effectiveness of measures to prevent and control the epidemic. Understanding the complex dynamics and characteristics that determine the duration of rumors is crucial for their effective management. In response to this urgent requirement, our study takes survival analysis method to analyze COVID-19 rumors comprehensively and rigorously. Our primary aim is to clarify the distribution patterns and key determinants of their persistence. Through this exploration, we aim to contribute to the development of robust rumor management strategies, thereby reducing the adverse effects of misinformation during the ongoing pandemic. The dataset utilized in this research was sourced from Tencent's "Jiao Zhen" Verification Platform's "Real-Time Debunking of Novel Coronavirus Pneumonia" system. We gathered a total of 754 instances of rumors from January 18, 2020, to January 17, 2023. The duration of each rumor was ascertained using the Baidu search engine. To analyze these rumors, survival analysis techniques were applied. The study focused on examining various factors that might influence the rumors' longevity, including the theme of the content, emotional appeal, the credibility of the source, and the mode of presentation. Our study's results indicate that a rumor's lifecycle post-emergence typically progresses through three distinct phases: an initial rapid decline phase (0-25days), followed by a stable phase (25-1000days), and ultimately, an extinction phase (beyond 1000days). It is observed that half of the rumors fade within the first 25days, with an average duration of approximately 260.15days. When compared to the baseline category of prevention and treatment rumors, the risk of dissipation is markedly higher in other categories: policy measures rumors are 3.58 times more likely to perish, virus information rumors have a 0.52 times higher risk, epidemic situation rumors are 4.86 times more likely to die out, and social current affairs rumors face a 2.02 times increased risk. Additionally, in comparison to wish rumors, bogie rumors and aggression rumors have 0.26 and 0.27 times higher risks of dying, respectively. In terms of presentation, graphical and video rumors share similar dissolution risks, whereas textual rumors tend to have a longer survival time. Interestingly, the credibility of the rumor's source does not significantly impact its longevity. The survival time of rumors is strongly linked to their content theme and emotional appeal, whereas the credibility of the source and the format of presentation have a more auxiliary influence. This study recommends that government agencies should adopt specific strategies to counter rumors. Experts and scholars are encouraged to take an active role in spreading health knowledge. It's important for the public to proactively seek trustworthy sources for accurate information. Media platforms are advised to maintain journalistic integrity, verify the accuracy of information, and guide the public towards improved media literacy. These actions, collectively, can foster a collaborative alliance between the government and the media, effectively combating misinformation.

Role of clinical pharmacists in multidisciplinary collaborative management of blood glucose in COVID-19 patients with hyperglycemia

BackgroundDuring the ongoing global pandemic of COVID-19, the association between hyperglycemia and COVID-19 infection has emerged as a notable concern. Therefore, finding effective methods to manage hyperglycemia in patients with COVID-19 is crucial. ObjectiveTo introduce the clinical pharmacists participating in multidisciplinary collaborative whole hospital blood glucose management mode, and to explore its effect on blood glucose control in patients with coronavirus disease 2019 infection and complicated with hyperglycemia. MethodsPatients with COVID-19 treated at Nanjing Drum Tower Hospital from December 2022 to January 2023 were assigned to routine diagnosis and treatment group and whole hospital blood glucose management group according to the blood glucose management plan received by patients. The groups were compared in regards to their adherence to management advice, blood glucose levels, fluctuation, inflammation-related indicators, medical service-related indicators, and incidence of hypoglycemia and adverse events. ResultsAfter 5 days of glucose management, both groups showed a decrease in fasting and postprandial blood glucose. Postprandial blood glucose in the whole hospital glucose management group was significantly lower than the routine group (P < 0.05). The whole hospital glucose management group showed a significant increase in compliance rate, improved inflammation-related indicators, and higher detection rates for hemoglobin and islet function (P < 0.05). Implementation rates for medical orders and treatment plans were also higher in the whole hospital group (P < 0.05). There was no significant difference in incidence of adverse events. ConclusionsMultidisciplinary blood glucose management is highly recommended for patients with COVID-19 who have hyperglycemia due to its effectiveness, standardization, safety, and improvement of inflammation indicators.

Longitudinal Assessment of Hair Cortisol as a Predictor of Psychological Symptoms During COVID-19

BackgroundThere is a lack of evidence regarding enduring psychoneuroendocrine changes following an initial traumatic event, particular in the presence of an ongoing stressor. The coronavirus pandemic presents an opportunity to explore this matter. Consequently, the purpose of the present study was to investigate the impact of the ongoing pandemic (2021) on individuals, who experienced a first-time motor vehicle crash (MVC) at least 6 years earlier. To this end, we hypothesized that hair cortisol concentrations (HCC) following a first-time traumatic event positively predict symptoms of depression. MethodWe investigated N = 69 individuals (18 – 65 yrs.), who were victims of a MVC during 2010 – 2014. Hair strands were collected 10 days (t1) and 3 months after the MVC (t2), as well during the pandemic in 2021 (t3). To assess symptoms of depression, the participants filled out the Beck Depression Inventory at t1 - t3 and were additionally interviewed (Structured Clinical Interview for DSM-IV Axis I) at t1 and t2. Exclusion criteria conveyed a lifetime or acute mental disorder (incl. past trauma exposure). ResultsElevated pre-pandemic HCC following adversity (i.e., MVC) significantly predicted symptoms of depression in adults during the coronavirus pandemic (BDI: ß =.44, p =.010, R2 =.20), even after controlling for confounders. HCC significantly decreased over time, while in average psychological symptoms remained consistent. ConclusionCortisol dysregulation in the past presents an enduring vulnerability to ongoing stress. In this regard, vulnerable groups may benefit from preventive measures. This finding validates the predictive power of HCC and extended past evidence in this regard, at the same time reinforcing the concept of the diathesis-stress model.

Tri-layer gradient structured micro/nanofibrous nonwovens for high filtration efficiency and low air resistance

The coexistence of PM2.5 pollution and the ongoing pandemic poses significant risks to human health. Protective masks incorporating high-efficiency particulate air filters offer adequate protection against PM2.5 particles. Micro/nanofibrous nonwovens, including melt-blown and electrospun nonwovens, are essential filter materials. This study focuses on the filtration mechanism and geometric structure design of multi-level structured micro/nanofibrous nonwovens. A comprehensive investigation was conducted on a filter core material composed of polyacrylonitrile, a polystyrene electrospun membrane, a polypropylene (PP) melt-blown membrane, and a supporting outer layer of a PP spun-bonded membrane. The resulting nonwovens exhibited exceptional filtration efficiency of 99.98% for PM0.3 particles, with a low pressure drop of 60 Pa at 32 L/min inlet air velocity. Filtration efficiencies of 99.99% and 100% were achieved for PM1.0 and PM2.5 particles, respectively. These characteristics make the designed composite nonwovens a very promising filter material for masks. The study contributes to understanding filtration mechanisms and developing advanced high-efficiency filter materials, enhancing protection against airborne pollutants.

Genome sequences of Vibrio cholerae strains isolated in the DRC between 2009 and 2012.

Vibrio cholerae has caused seven cholera pandemics in the past two centuries. The seventh and ongoing pandemic has been particularly severe on the African continent. Here, we report long read-based genome sequences of six V. cholerae strains isolated in the Democratic Republic of the Congo between 2009 and 2012.

Fear of giving birth alone: Experiences of psychological distress, symptoms of anxiety and depression, and coping- strategies of childbearing women during COVID-19

BackgroundPsychological distress during pregnancy is a well-documented risk factor for adverse maternal outcomes. Distress related to the COVID-19 pandemic may further increase the vulnerability of pregnant women to negative mental health outcomes. AimTo explore the mental health experiences of pregnant women, focusing on mental health outcomes, challenges related to the pandemic, coping strategies, and factors buffering mental health factors during the restricted COVID-19 lockdown period. MethodsA mixed-methods survey study was conducted, examining symptoms of anxiety, depression, and burnout among 21 pregnant women. Qualitative data were gathered through open-ended questions about participants’ experiences of challenges, coping strategies and buffering factors amid the pandemic. Symptoms of anxiety, depression and burnout were calculated, and qualitative data was thematically analyzed. ResultsApproximately one-third (24 %) of the respondents reported clinically significant levels of depression, 19 % reported clinically significant levels of anxiety, and 43 % reported experiencing burnout. All participants reported distress and emotional burden, including fear, worry, stress and anxiety related to the pandemic. Specific concerns such as fear of giving birth alone, fear of the consequences due to lockdown restrictions, insufficient information, disruption of prenatal healthcare services, and fear of miscarriage were prevalent among the participants. Social support, financial stability, stable relationships, adherence to daily routines, reduced stress and social demands, a calmer daily life, physical activity, and less work-related stress including working from home, emerges as buffering factors that aided women in coping with pandemic-related distress. ConclusionHealthcare providers should prioritize stability, predictability, and minimizing disruptions to prenatal care. Broad-based screening is crucial to identify women at risk of depression, anxiety, and burnout. Recommendations for clinical pathways aimed at pregnant women are discussed.

Falsification of home rapid antigen lateral flow tests during the COVID-19 pandemic

During the COVID-19 pandemic, lateral flow tests (LFTs) were used to regulate access to work, education, social activities, and travel. However, falsification of home LFT results was a concern. Falsification of test results during an ongoing pandemic is a sensitive issue. Consequently, respondents may not answer truthfully to questions about LFT falsification behaviours (FBs) when asked directly. Indirect questioning techniques such as the Extended Crosswise model (ECWM) can provide more reliable prevalence estimates of sensitive behaviors than direct questioning. Here we report the prevalence of LFT FBs in a representative sample in England (n = 1577) using direct questioning (DQ) and the ECWM. We examine the role of demographic and psychological variables as predictors of LFT FBs. We show that the prevalence estimates of the FBs in the DQ condition were significantly lower than the ECWM estimates, e.g., reporting a negative result without conducting a test: 5.7% DQ vs 18.4% ECWM. Moral norms, subjective norms, anticipated regret, perception of risk to self, and trust in government predicted some of the FBs. Indirect questioning techniques can help provide more realistic and higher quality data about compliance with behavioural regulations to government and public health agencies.

Rapid evidence assessment of student-assisted assessment and brief intervention clinics: Addressing the gaps in rural and remote health care.

With high disease and disability burden in rural and remote regions, student-assisted clinics can be an effective workforce development tool to meet community health needs and workforce shortages. This research sought to identify the conditions under which student-assisted clinics can be successfully utilised as a workforce development strategy, with specific application to remote Queensland, Australia. A rapid review of the international literature in English was conducted. This was the most appropriate type of review because the results of the review were time-sensitive, with the student-assisted clinic model being trialled in Queensland soon. A mixed methods design was applied, with the search strategy piloted with one database. Eleven studies met the inclusion criteria. Seven reported data on participant experiences, including consumers, students, services/clinics, and educators/supervisors/health professionals. Each of the studies operationalised student-assisted clinics through practice models (university-driven learning need), service delivery models (service driven need addressed through a student workforce), community need models (student delivered services primarily addressing a community health need), and blended models (practice need and community need). Some studies reported concerns about fragmentation of services, referral pathways and issues with follow-up, while others reported concerns about sustainable funding. All models reported successful outcomes when focused on service or consumer health outcomes, or student learning outcomes. Student-assisted clinics make an important contribution to the development of the rural and remote health workforce. Student-assisted clinics can complement and extend existing services, supporting workforce development in an overstretched health system impacted by an ongoing pandemic.