Management of centralized transition ward in a large general hospital in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant infection.

The epidemic of coronavirus disease 2019 (COVID-19) has seriously affected people's normal work, life, and medical treatment. Since Mar. 2022, there has been a pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant in Shanghai. In order to meet the needs of hospitalization for patients, and at the same time for better control of epidemic and nosocomial infections, a large hospital in Shanghai innovatively set up a centralized transition ward in the hospital, and established scientific rules of medical work, regulations for prevention of nosocomial infections and efficient norms for patient admission. During the operation of the ward, a total of 211 patients were treated and one of the patients was confirmed of COVID-19 recurrence. All work was carried out methodically, and neither hospitalized patients nor medical staff had nosocomial infection of COVID-19. The preparation, operation and management of the central transition ward in our hospital are summarized here to provide guidance and reference for general hospitals to carry out similar work under the epidemic.Copyright © 2022, Second Military Medical University Press. All rights reserved.

Evaluation of COVID-19 Vaccines in Primary Prevention Against Infections and Reduction in Severity of Illness Following the Outbreak of Sars-Cov-2 Omicron Variant in Shanghai

Objectives: To evaluate COVID-19 vaccines in primary prevention against infections and lessening the severity of illness following the most recent outbreak of the SARS-CoV-2 Omicron variant in Shanghai. Method(s): To investigate whether inactivated vaccines were effective in protecting against COVID-19 infections, we estimated the odds ratio (OR) of the vaccination in COVID-19 cases vs. matched community-based healthy controls. To evaluate the potential benefits of vaccination in lowering the risk of symptomatic infection (vs. asymptomatic), we estimated the relative risk (RR) of symptomatic infections among diagnosed patients. We also applied the multivariate stepwise Logistic regression analyses to measure the risk of disease severity (symptomatic vs. asymptomatic and moderate/severe vs. mild) in COVID-19 patient cohort with vaccination status as an independent variable while controlling for potential confounding factors. Result(s): Out of the 153,544 COVID-19 patients included in the analysis, 118,124 (76.9%) patients had been vaccinated and 143,225(93.3%) were asymptomatic patients. Of the 10,319 symptomatic patients, 10,031(97.2%), 281(2.7%) and 7(0.1%) experienced mild, moderate, and severe infections, respectively. There is no evidence that the vaccination helped protect from infections (OR=0.82, p=0.613). The vaccination, however, offered a small but significant protection against symptomatic infections (RR=0.92, p < 0.001) and halved the risk of moderate/severe infections (OR=0.48, 95% CI: 0.37 - 0.61). Older age (> 60 years) and malignant tumors were significantly associated with moderate/severe infections. Gender also appeared to be a risk factor for symptomatic infections, with females being associated with a lower risk for moderate/severe illness. Conclusion(s): Inactivated COVID-19 vaccines helped provide a small but significant protection against symptomatic infections and halved risk of moderate/severe illness among symptomatic patients. The vaccination was not effective in blocking COVID-19 Omicron variant community spread.Copyright © 2023

Clinical Characteristics of Patients with Sars-Cov-2 Omicron Variant and COVID-19 Vaccination with Clinical Outcome Findings in Shanghai, China: A Single Center, Retrospective, Observational Study

Objectives: This study aimed to determine disease severity, clinical features, clinical outcome in hospitalized patients with the Omicron variant and evaluate the effectiveness of one-dose, two-dose, and three-dose inactivated vaccines in reducing viral loads, disease course, ICU admissions and severe diseases. Method(s): Retrospective cohort analysis was performed on 5,170 adult patients (>=18 years) identified as severe acute respiratory syndrome coronavirus 2 positive with Reverse Transcription Polymerase Chain Reaction admitted at Shanghai Medical Center for Gerontology between March 2022 and June 2022. COVID-19 vaccination effectiveness was assessed using logistic regression models evaluating the association between the risk of vaccination and clinical outcomes, adjusting for confounders. Result(s): Among 5,170 enrolled patients, the median age was 53 years, and 2,861 (55.3%) were male. 71.0% were mild COVID-19 cases, and cough (1,137 [22.0%]), fever (592 [11.5%]), sore throat (510 [9.9%]), and fatigue (334 [6.5%]) were the most common symptoms on the patient's first admission. Ct values increased generally over time and 27.1% patients experienced a high viral load (Ct value< 20) during their stay. 105(2.0%) of these patients were transferred to the intensive care unit after admission. 97.1% patients were cured or showed an improvement in symptoms and 0.9% died in hospital. The median length of hospital stay was 8.7+/-4.5 days. In multivariate logistic analysis, booster vaccination can significantly reduce ICU admissions and decrease the severity of COVID-19 outcome when compared with less doses of vaccine (OR=0.75, 95%CI, 0.62-0.91, P<=0.005;OR=0.99, 95%CI, 0.99-1.00, p<0.001). Conclusion(s): In summary, the most of patients who contracted SARSCoV-2 omicron variant had mild clinical features and patients with vaccination took less time to lower viral loads. As the COVID-19 pandemic progressed, an older and less vaccinated population was associated with higher risk for ICU admission and severe disease.Copyright © 2023

The Economic Impact of COVID-19 Crisis Based on Complex Network Evolution Model

The Covid-19 crisis impacts the economy with non-equilibrium and non-linear shocks. This paper builds a trading network model based on the theory of trading economics. Using the network model, the evolutionary procedure of the economic depression triggered by the shocks are researched. The study shows that under the impact of shocks, small and medium-sized trading agents with weak profitability will first experience cash flow crisis. Then the crisis contagion is formed in upstream and downstream through the trading network. The credit reduction caused by the business deterioration will make the interest rate in the economy increase and promote each other with the bankruptcy of trading entities. Eventually, it leads to the feedback loop in liquidity crisis and debt crisis, which accelerates the bankruptcy of enterprises and possibly causing a debt crisis in the banking sector. It is found that after the shock, the economic recovery may take three patterns: stable recovery, slow recession and secondary crisis. Finally, the paper proposes relevant policy recommendations to reduce the impact of the crisis. © 2023 Editorial Borad of Complex Systems and Complexity Science. All rights reserved.

Sustainable development in natural resources industry: is geopolitical risk a catalyst for corporate excess cash holdings?

With the outbreak of the Russia-Ukraine conflict, combined with the COVID-19 epidemic and the Federal Reserve's interest rate hike, geopolitical risks have increased sharply, which has brought great pressure on the sustainable development of natural resources industry. This study aims to discuss the impact of geopolitical risk (GPR) on corporate excess cash holdings in China's natural resources industry. The findings suggest that GRP can encourage enterprises in the natural resources industry to hold more excess cash. The findings still hold with a suite of robustness tests. The study also evidences that the above effect is more significant for state-owned enterprises, enterprises in the mining industry, and large-scale enterprises. Finally, further results show that with the increase of GPR, enterprises with strong risk-taking capacity tend to hold more excess cash, while enterprises registered in higher market-oriented regions are inclined to retain less excess cash. These findings can conduce to a deep understanding of the influence of GPR on corporate excess cash holdings and serve as a reference for policy-makers to adjust policies. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

A Hybrid Model of Deep Learning Features and Clinical Features for Severe Cases Predication of COVID-19

COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.

U.S. Public Transportation Ridership Analysis and Prediction based on COVID-19

In this paper, a research was conducted to analyse and predict the impacts of COVID-19 on public transportation ridership in the U.S. and 5 most populous cities of the U.S. (New York City, Los Angeles, Chicago, Houston, Philadelphia). The paper aims to exploit the correlation between COVID-19 and public transportation ridership in the U.S. and make the reasonable prediction by machine learning models, including ARIMA and Prophet, to help the local governments improve the rationality of their policy implementation. After correlation analyses, high level of significant and negative correlations between monthly growth rate of COVID-19 infections and monthly growth rate of public transportation ridership are decidedly validated in the total U.S., and New York City, Los Angeles, Chicago, Philadelphia, except Houston. To analyse the errors of Houston, we consult the literature and made a discussion of Influencing factors. We find that the level of public transportation in quantity and utilization is terribly low in Houston. In addition, the factors, such as the lack of planning law and estimation of urban expressways, the high level of citizens' dependence on private cars and pride of owning cars play a considerable roll in the errors. And the impacts can be predicted to a certain extent through two forecasting models (ARIMA and Prophet), although the precision of our models is not enough to make a precise forecast due to the limitations of model tuning and model design. According to the comparison of the two models, ARIMA models' forecasting accuracy is between 6% and 10%, and Prophet's forecasting accuracy is between 8%-12%, depending on the city. Since the insufficient stationarity, periodicity, seasonality of time series, the Prophet models are hard be more refined. © 2023 SPIE.

Tackling Covid-19, the Singapore Way

The Singapore government's strategy of managing the COVID-19 crisis in its first year was built upon its experience of handling the SARS crisis in 2003. The strategy showcased extensive capacities to test, trace, isolate, treat, and limit the importation of cases. Implementation was impressive at first, delivering results that were internationally admired. An emphasis on building social responsibility through transparent communication and the use of technology has also overall been successful. To keep the economy afloat and ready to thrive in postpandemic times, the government allocated approximately $100 billion in a budget that was business- and employer-centric, consistent with neoliberal globalization. Where the government failed was in its handling of the disease outbreaks in migrant worker dormitories, which it seemed not to have anticipated even though there had been warnings from civil society activists. Opportunistically, the PAP called for early parliamentary elections, arguing that a PAP government with a strong mandate was what successful management of this "crisis of a generation” required. Since Singaporeans are often thought to be risk-averse, there was an expectation of a landslide victory for the PAP with voters taking a "flight to safety” and supporting the only government they had ever known in this dominant-party system. Instead, the PAP lost significant vote share and 10 seats including two multi-member "group representation constituencies” were lost to the opposition. If these results reflected what voters thought of the government's performance in managing the COVID-19 crisis, they may well point more specifically to public dissatisfaction with immigration policy and the dormitories fiasco. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

The Association between the Fibrosis-4 Index and COVID-19: A Systematic Review and Meta-Analysis

Objective. The prognosis value of fibrosis-4 score (FIB-4) in COVID-19 is controversial. Hence, we conducted a systematic review and meta-analysis to investigate the association between the FIB-4 index and COVID-19 disease progression. Methods. We performed meta-analysis using the PubMed, Embase, and Cochrane databases. A fixed- or random-effects model was used for evaluating heterogeneity. Results. Thirteen studies were included. The meta-analysis of unadjusted results showed that compared to lower FIB-4 index, patients with higher FIB-4 index had increased odds of mortality (OR=5.1, 95%CI 3.67-7.09;P<0.001), ICU admission (OR=2.32, 95%CI: 1.65-3.25, P<0.00001) and need for mechanical ventilator support (OR=3.51, 95%CI: 2.1-5.85, P<0.001). In addition, the meta-analysis of adjusted results showed patients with higher FIB-4 index was associated with increased risk of mortality (OR=3.01, 95%CI: 2.21-4.09, P<0.001) and need for mechanical ventilator support (OR=3.76, 95%CI: 2.08-6.82, P<0.001) compared to patients with lower FIB-4 index. Conclusion. This meta-analysis indicated that high FIB-4 index score was associated with the severity and mortality in COVID-19 infected patients.Copyright © 2022 by the Association of Clinical Scientists, Inc.

What are the experiences of supportive care in people affected by brain cancer and their informal caregivers: A qualitative systematic review.

PURPOSE: To critically synthesise qualitative research to understand experiences of supportive care in people affected by brain cancer and their informal caregivers. METHODS: A qualitative systematic review was conducted according to the Joanna Briggs methodology and has been reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) Guidelines. Electronic databases were searched by an expert systematic review librarian for all qualitative studies irrespective of research design. All publications were double screened by two reviewers using a pre-determined exclusion and inclusion criteria. The review was managed using Covidence systematic review software. Methodological quality assessment and data extraction were performed. Qualitative findings accompanied by illustrative quotes from included studies were extracted and grouped into categories, which created the overall synthesised findings. RESULTS: A total of 33 studies were included which represented a total sample of 671 participants inclusive of 303 patients and 368 informal caregivers. There was a total of 220 individual findings included in this review, which were synthesised into two findings (1) caregivers and patients perceived supports which would have been helpful and (2) caregiver and patient experiences of unmet supportive care needs. CONCLUSION: This review highlighted the suffering and distress caused by brain cancer and associated treatments. Both patients and their informal caregivers experienced disconnect from themselves in renegotiating roles, and a profound sense of loneliness as the physical deterioration of the disease progressed. Both patients and informal caregivers reported similar unmet needs within the current service provision for brain cancer. However, what is apparent is that current cancer services are provided solely for patients, with little or no consideration to the support needs of both the patient and their informal caregiver. Service re-design is needed to improve care coordination with individualised informational support, implementation of holistic needs assessments for both the patients and their caregivers, better community support provision, improved opportunities for emotional care with early referral for palliative care services. IMPLICATIONS FOR CANCER SURVIVORS: It is recommended that members of the multidisciplinary brain cancer team reflect on these findings to target holistic needs assessments and develop shared self-management care plans for both the patient and the informal caregiver.

Disruptions to in-person medical visits across the United States during the COVID-19 pandemic: evolving disparities by medical specialty and socio-economic status.

OBJECTIVES: This study aimed to investigate how people's health-seeking behaviors evolve in the COVID-19 pandemic by community and medical service category. STUDY DESIGN: This is a longitudinal study using mobility data from 19 million mobile devices of visits to all types of health facility locations for all US states. METHODS: We examine the variations in weekly in-person medical visits across county, neighborhood, and specialty levels. Different regression models are used for each level to investigate factors that influence the disparities in medical visits. County-level analysis explores associations between county medical visit patterns, political orientation, and COVID-19 infection rate. Neighborhood-level analysis focuses on neighborhood socio-economic compositions as potential determinants of medical visit levels. Specialty-level analysis compares the evolution of visit disruptions in different specialties. RESULTS: A more left-leaning political orientation and a higher local infection rate were associated with larger decreases in in-person medical visits, and these associations became stronger, moving from the initial period of stay-at-home orders into the post-lockdown period. Initial reactions were strongest for seniors and those of high socio-economic status, but this reversed in post-lockdown period where socio-economically disadvantaged communities stabilized at a lower level of medical visits. Neighborhoods with more female and young people exhibited larger decreases in in-person medical visits throughout the initial and post-lockdown periods. The evolution of disruptions diverges across medical specialties, from only short-term disruption in specialties such as dentistry to increasing disruption, as in cardiology. CONCLUSIONS: Given distinct patterns in visit between communities, medical service categories, and between different periods in the pandemic, policy makers, and providers should concentrate on monitoring patients in disrupted specialties who overlap with the at-risk contexts and socio-economic factors in future health emergencies.

Community Diversity Climate Impact on the Well-Being of Asian Americans Amid Anti-Asian Sentiment During the COVID-19 Pandemic: The Role of Two-Way Symmetrical Communication by Local Governments

Integrating the literature from public relations, community psychology, and minority stress theory, this study proposes and tests a model that describes the mechanism by which local governments' two-way symmetrical communication practices may affect local Asian Americans' experiences amid anti-Asian sentiment during the COVID-19 pandemic. The results of an online survey of 400 Asian Americans living in the United States indicated that local governments' two-way symmetrical communication practices with respect to diversity acceptance helped establish a community diversity climate. Such a perception lowered the target group's perceived stigma consciousness and fear of discrimination, which are the two main proximal stressors affecting minority members' emotions and wellbeing. Theoretical and practical implications on public relations and governmental communication are discussed.

Can internet concern about COVID-19 help predict stock markets: new evidence from high-concern and low-concern periods

The unprecedented outbreak of Corona Virus Disease 2019 (COVID-19) has resulted in extreme volatility in stock markets. This study mainly examines the predictive ability of the Internet concern about COVID-19 on stock index returns, based on the framework of GARCH type models. Instead of using the whole sample period, we divide the Internet concern about COVID-19 into high-concern and low-concern periods by breakpoint test method and then examine its predictive ability for stock returns in different periods, respectively. Using stock indexes of 10 countries and abnormal Google search volume of 'coronavirus' as study samples, the results reveal that (1) the Internet concern about COVID-19 has a negative impact on the stock index returns in the whole and high-concern periods, while its influence in the low-concern period is mixed;(2) the Internet concern about COVID-19 improves the prediction accuracy of stock index returns in the high-concern period, while seems to lose its powerful predictive ability in the whole and low-concern periods.

[Research progress on the relationship between low-density neutrophils and infectious diseases]

Neutrophils play an important role in infectious diseases by clearing pathogens in the early stages of the disease and damaging the surrounding tissues along with the disease progress. Low-density neutrophils (LDNs) are a crucial and distinct subpopulation of neutrophils. They are a mixture of activated and degranulated normal mature neutrophils and a considerable number of immature neutrophils prematurely released from the bone marrow. Additionally, they may be involved in the occurrence and development of diseases through the changes in phagocytosis, the generation of reactive oxygen species (ROS), the enhancement of the ability to produce neutrophils extracellular traps and immunosuppression. We summarizes the role of LDNs in the pathogenesis and their correlation with the severity of infectious diseases such as COVID-19, severe fever with thrombocytopenia syndrome (SFTS), AIDS, and tuberculosis.

Establishing Academic Identities Through Professional Socialisation During the COVID-19 Pandemic: A Doctoral Student, Institutional Member, or Early Career Researcher?

While higher education institutions promptly responded to the transition to online or blended practices as a result of COVID-19, there is limited current understanding of how first-year PhD students committed themselves to various online networking experiences during their initial stage of professional development. By drawing on Kolb's experiential learning cycle, this chapter elicits two first-year international PhD students' professional trajectories of forming our professional identities in academia during the COVID-19 pandemic. Despite engaging with different professional socialisation activities, we both underwent three transformative stages which we classify as acquiring knowledge, establishing networks, and gaining validation. Our findings indicate that our dynamic and consecutive professional identity formation transitioned through three stages: a doctoral student, an institutional member, and an early career researcher. This chapter reveals how this linear three-stage process respectively unfolds for different international doctoral students. In this regard, relevant implications are proposed for current and prospective international doctoral students and their institutions to refer to in better facilitating international doctoral students' professional identity development during and beyond COVID-19 pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.

The application of deep learning for COVID-19 diagnosis and treatment

The coronavirus disease 2019 (COVID-19) pandemic has resulted in a considerable increase in hospitalizations, leading to an increasing demand for accurate and efficient techniques for diagnosis. The CT-based diagnosis can provide pathologic information to assist treatment but be restricted due to inefficient and relatively complicated implementation. With the advent of deep learning and advanced hardware, an AI-assisted method diagnosis and segmentation for COVID-19 are proposed. In this paper, many traditional machine learning methods for imaging classification and segmentation are discussed, such as k-Nearest Neighbours (KNN), support vector machines (SVM), edge-based or region-based segmentation. In addition, we proposed a ResNet-based model and an improved U-Net for medical tasks of classification and segmentation, respectively. Our proposed model achieved desirable accuracy in medical applications. © 2023 SPIE.

Traffic Flow Forecasting in the COVID-19: A Deep Spatial-temporal Model Based on DiscreteWavelet Transformation

Traffic flowprediction has always been the focus of research in the field of Intelligent Transportation Systems, which is conducive to the more reasonable allocation of basic transportation resources and formulation of transportation policies. The spread of COVID-19 has seriously affected the normal order in the transportation sector. With the increase in the number of infected people and the government's anti-epidemic policy, human outgoing activities have gradually decreased, resulting in increasingly obvious discreteness and irregularities in traffic flow data. This article proposes a deep-space time traffic flow prediction model based on discrete wavelet transform (DSTM-DWT) to overcome the highly discrete and irregular nature of the new crown epidemic. First, DSTM-DWT decomposes traffic flow into discrete attributes, such as flow trend, discrete amplitude, and discrete baseline. Second, we design the spatial relationship of the transportation network as a graph and integrate the new crown pneumonia epidemic data into the characteristics of each transportation node. Then, we use the graph convolutional network to calculate the spatial correlation of each node, and the temporal convolutional network to calculate the temporal correlation of the data. In order to solve the problem of high discreteness of traffic flow data during the epidemic, this article proposes a graph memory network (GMN), which is used to convert discrete magnitudes separated by discrete wavelet transform into highdimensional discrete features. Finally, use DWT to segment the predicted traffic data, and then perform the inverse discrete wavelet transform between the newly segmented traffic trend and discrete baseline and the discrete model predicted by GMN to obtain the final traffic flow prediction result. In simulation experiments, this work was compared with the existing advanced baselines to verify the superiority of DSTM-DWT.

Technology growth in the digital age: Evidence from China

Based on a broad definition of the digital economy, this study constructs a digital technology capital measure-ment framework and measures China's digital technology capital services from 2002 to 2018 to illustrate the growth of digital technology. In addition, the relationship between digital technology and economic growth is examined using the growth accounting method and econometric panel models from the perspective of the substitution and penetration effects of information and communication technologies. The measurement results show that China's digital technology capital services grew faster than non-digital ones from 2002 to 2018. In terms of the substitution effect, the contribution of traditional capital to economic growth is diminishing, whereas digital technology capital's contribution is expanding. Regarding the penetration effect, the contribution of digital technology capital to labor productivity is gradually overtaking that of traditional capital, with the impact varying by sector. These results may provide developing countries with a strategy to seek new devel-opment drivers and sustain economic growth in the post-pandemic era.

Management of centralized transition ward in a large general hospital in Shanghai during the epidemic caused by severe acute respiratory syndrome coronavirus 2 omicron variant infection

The epidemic of coronavirus disease 2019 (COVID-19) has seriously affected people's normal work, life, and medical treatment. Since Mar. 2022, there has been a pandemic of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) omicron variant in Shanghai. In order to meet the needs of hospitalization for patients, and at the same time for better control of epidemic and nosocomial infections, a large hospital in Shanghai innovatively set up a centralized transition ward in the hospital, and established scientific rules of medical work, regulations for prevention of nosocomial infections and efficient norms for patient admission. During the operation of the ward, a total of 211 patients were treated and one of the patients was confirmed of COVID-19 recurrence. All work was carried out methodically, and neither hospitalized patients nor medical staff had nosocomial infection of COVID-19. The preparation, operation and management of the central transition ward in our hospital are summarized here to provide guidance and reference for general hospitals to carry out similar work under the epidemic.Copyright © 2022, Second Military Medical University Press. All rights reserved.

The Structural Relationships among Academic Pressure, Independent Learning Ability, and Academic Self-Efficacy

Background: The coronavirus disease pandemic has caused significant disruption in the field of education, resulting in the need for more online classes and a blended offline and online teaching model. Therefore, understanding what makes this model effective is important. Accordingly, this study explored the structural relationships among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the pandemic and independent learning ability's mediating effect on the relationship between academic pressure and academic self-efficacy. Methods: Adopting a random sampling method, this study surveyed 761 Chinese college, Shaanxi Province, China in 2022 and university students. Factor analysis, correlation analysis, structural equation modeling, and path analysis were used to analyze the data. Results: The results show that the academic pressure faced by Chinese English majors had a significant negative impact on academic self-efficacy (P<0.001). However, academic pressure had no statistical effect on stu-dents' independent learning ability (P=0.317). Moreover, independent learning ability had a significant positive effect on academic self-efficacy (P<0.001) and a mediating effect on the relationship between academic pressure and academic self-efficacy (P=0.032). Conclusion: Independent learning ability can directly and indirectly affect academic self-efficacy. Thus, in an online and offline blended teaching model, teachers should guide students regarding self-exploration, com-munication, and cooperation based on existing knowledge and experience. They should also enable students to improve their learning process and independent learning ability. Various language learning situations should be established for learning English so that by experiencing success and failure, students can ultimately improve their academic self-efficacy. © 2023 Zhao et al.