The impact of virtual learning on students' educational behavior and pervasiveness of depression among university students due to the COVID-19 pandemic.

BACKGROUND: One of the worst pandemics of recent memory, COVID-19, severely impacted the public. In particular, students were physically and mentally affected by the lockdown and the shift from physical person-to-person classrooms to virtual learning (online classes). This increased the prevalence of psychological stress, anxiety, and depression among university students. In this study, we investigated the depression levels in Saudi Arabian university students who were learning virtually because of the COVID-19 pandemic and examined its impact on their educational proficiency. METHODS: The study focused on two points: first, examining the depression levels among undergraduate students in Saudi Arabia, by adapting the Zung (Self-Rating Depression Scale) questionnaire. Second, whether there is an association between the levels of depression and various distress factors associated with virtual (online) learning resulting from the COVID-19 pandemic and its impact on students' educational behaviors. The questionnaire was prepared using a monkey survey and shared online, via email, and on WhatsApp groups, with participants in two universities, a public and private university in the largest city of Saudi Arabia. A total of 157 complete responses were received. Data were analyzed using SPSS-24, the chi-square test, descriptive statistics, and multilinear regression. RESULTS: The results indicated that three-fourths of the university students suffered from different depressive symptoms, half of which had moderate to extreme levels of depression. Our study confirmed that a boring virtual (online) learning method, stress, fear of examinations, and decreased productivity were significantly associated with increased depression. In addition, 75% and 79% of the students suffered from stress and fear of examinations, respectively. About half of the students were associated with increased depression. The outcome also indicated that female students experienced extreme depression, stress, and fear of examinations more than males. CONCLUSION: These findings can inform government agencies and representatives of the importance of making swift, effective decisions to address students' depression levels. It is essential to provide training for students to change their educational experience mindset, which might help decrease "depression and stress-related growth." There is also a need to search for a better virtual teaching delivery method to lessen students' stress and fear of examinations.

Methods for Improving the Variance Estimator of the Kaplan-Meier Survival Function, When There Is No, Moderate and Heavy Censoring-Applied in Oncological Datasets.

In case of heavy and even moderate censoring, a common problem with the Greenwood and Peto variance estimators of the Kaplan-Meier survival function is that they can underestimate the true variance in the left and right tails of the survival distribution. Here, we introduce a variance estimator for the Kaplan-Meier survival function by assigning weight greater than zero to the censored observation. On the basis of this weight, a modification of the Kaplan-Meier survival function and its variance is proposed. An advantage of this approach is that it gives non-parametric estimates at each point whether the event occurred or not. The performance of the variance of this new method is compared with the Greenwood, Peto, regular, and adjusted hybrid variance estimators. Several combinations of these methods with the new method are examined and compared on three datasets, such as leukemia clinical trial data, thalassaemia data as well as cancer data. Thalassaemia is an inherited blood disease, very common in Pakistan, where our data are derived from.

Prevalence of COVID-19 Pandemic, Self-Esteem and Its Effect on Depression Among University Students in Saudi Arabia.

Background and Aims: University students are commonly identified as susceptible, suffering from higher anxiety, stress, and depression than the overall population. During the Corona Virus Disease pandemic (COVID), education was shifted to the virtual learning environment. Students' ambiguity regarding academic accomplishment, imminent careers, changes in social life, and other concerns all these factors played a role in amplifying their stress levels, anxiety, and depression worldwide. This study investigates university students' self-esteem and depressions after they have been online learning for over 1 year due to the COVID-19 pandemic in Saudi Arabia. Methods: For this research, an adapted questionnaire of Rosenberg (Self-Esteem Scale) and Zung (Self-Rating Depression Scale) was used to get the responses of the participants of public and private universities in Saudi Arabia during March-April 2021. We received a total of 151 valid responses from respondents. For data analysis, we used descriptive statistics, ANOVA, multiple regression and binary logistic regression. Findings: The results showed that 75% of the students experienced different levels of depressions, with half (37.5%) having moderate to extreme levels of depression. A total of 41% of students experienced low self-esteem (38% females and 45% males). The regression results indicated depressive symptoms for low self-esteem. Furthermore, results of logistic regression showed that high self-esteem reduces the chances of getting depressive symptoms by 17%. The depressive symptoms were higher in female students than their male counterparts; furthermore, males experienced depressive symptoms less than females by 38%. Conclusions: Based on the current research results, it is concluded that the presence of the COVID-19 pandemic has dramatically increased the depressive symptoms in students, especially in female students. The findings suggested instant consideration and support for students. It is also suggested to the quest for potential managing policies that have been known and effective during the pandemic. Moreover, training should be provided for students to shift their educational experience mindset to an adaptive mindset, which can help them adapt to the new ways of learning and education.

The Global South political economy of health financing and spending landscape - history and presence.

The Global South nations and their statehoods have presented a driving force of economic and social development through most of the written history of humankind. China and India have been traditionally accounted as the economic powerhouses of the past. In recent decades, we have witnessed reestablishment of the traditional world economic structure as per Agnus Maddison Project data. These profound changes have led to accelerated real GDP growth across many LMICs and emerging countries of the Global South. This evolution had a profound impact on an evolving health financing landscape. This review revealed hidden patterns and explained the driving forces behind the political economy of health spending in these vast world regions. The medical device and pharmaceutical industry play a crucial role in addressing the unmet medical needs of rising middle class citizens across Asia, Latin America, and Africa. Domestic manufacturing has only been partially meeting this ever rising demand for medical services and medicines. The rest was complemented by the participation of multinational pharmaceutical industry, whose focus on investment into East Asia and ASEAN nations remains part of long-term market access strategies. Understanding of the past remains essential for the development of successful health strategies for the present. Political economy has been driving the evolution of health financing landscape since the establishment of early modern health systems in these countries. Fiscal gaps these governments face in diverse ways might be partially overcome with the spreading of cost-effectiveness based decision-making and health technology assessment capacities. The considerable remaining challenges ranging from insufficient reimbursement rates, large out-of-pocket spending, and lengthy lag in the introduction of cutting-edge technologies such as monoclonal antibodies, biosimilars, or targeted oncology agents, might be partially resolved only in the long run.

Detecting space-time disease clusters with arbitrary shapes and sizes using a co-clustering approach.

Ability to detect potential space-time clusters in spatio-temporal data on disease occurrences is necessary for conducting surveillance and implementing disease prevention policies. Most existing techniques use geometrically shaped (circular, elliptical or square) scanning windows to discover disease clusters. In certain situations, where the disease occurrences tend to cluster in very irregularly shaped areas, these algorithms are not feasible in practise for the detection of space-time clusters. To address this problem, a new algorithm is proposed, which uses a co-clustering strategy to detect prospective and retrospective space-time disease clusters with no restriction on shape and size. The proposed method detects space-time disease clusters by tracking the changes in space-time occurrence structure instead of an in-depth search over space. This method was utilised to detect potential clusters in the annual and monthly malaria data in Khyber Pakhtunkhwa Province, Pakistan from 2012 to 2016 visualising the results on a heat map. The results of the annual data analysis showed that the most likely hotspot emerged in three sub-regions in the years 2013-2014. The most likely hotspots in monthly data appeared in the month of July to October in each year and showed a strong periodic trend. A Correction has been published: https://doi.org/10.4081/gh.2023.1232