The effect of anxiety on thriving levels of university students during the COVID-19 pandemic

Enhancing smart healthcare with female students' stress and anxiety detection using machine learning

Machine learning (ML) is widely used to predict and detect stress and anxiety. Early detection of stress or anxiety is crucial for clinical pathways to enhance the supportive environment in society, particularly among female students. This study aims to assess and improve the accuracy of detecting stress and anxiety among female students using machine learning algorithms and functions. Three primary features are cigarette smoking, physical activity and grade point average (GPA). The multiple linear regression analysis conducted on 160 datasets obtained from the State-Trait Anxiety Inventory (STAI) at the University of Belgrade was selected. A heat map was utilised to identify the least engaging areas of the model along with most state anxiety factors. Additionally, R-squared (R2), mean absolute error (MAE), mean squared error (MSE) and root mean squared error (RMSE) were employed to assess the errors of the linear regression model for both pre-intervention and post-intervention, focusing on key features related to female students' anxiety. Using the K-Means algorithm, cluster analysis was executed on samples (N = 160) with three key features. The total average anxiety score was 44.39% (out of 80%) and is considered moderate. The heat map indicated a strong relationship between the variables. Overall, the post-intervention stage yielded acceptable results compared to the pre-intervention stage. Two clusters of anxiety among female students were identified, demonstrating that these features can accurately detect anxiety in female students. This research aims to analyse female students' stress and anxiety better using the linear regression algorithm. Additionally, ML functions demonstrated that smoking cigarettes, physical activity and GPA related to the stress and anxiety of female students have reduced errors during anxiety detection.

Exploring the impact of digital concept mapping methods on nurse students' learning anxiety, learning motivation

In involuntary distance education, like during epidemics and wars, students often feel heightened learning anxiety, impacting outcomes. Despite innovative teaching methods, many face hurdles in distance learning. We want to propose specific strategies to solve learning difficulties in distance education.

AIM

This study explored whether using digital concept maps (DCM) in physiology courses can reduce learning anxiety among nursing students.

DESIGN

The study was quasi-experimental, including a pre-and post-test control group.

METHODS

71 nursing students aged 16-18 enrolled in a physiology course were recruited in the study. DCM was the intervention as a tool for in-person learning (first 12 weeks) and distant learning (final six weeks). Each student was required to complete the assignments independently to compare learning outcomes. Questionnaires were administered, and an assignment evaluation was completed before and after the course's different formats.

RESULTS

DCM using software using mobile vehicles (mobile, notebook, pad) is digital learning to help nursing students learn difficult subjects. DCM improved the students' learning motivation and effectiveness more in distance learning than in-person learning, decreasing learning anxiety in both face-to-face and distance learning.

CONCLUSIONS

DCM promoted students' self-regulated learning and positively affected learning outcomes by increasing motivation and reducing stress. This study offers a tailored teaching framework for international settings to reduce student anxiety and improve learning effectiveness.

Depression, anxiety, and student satisfaction with university life among college students: a cross-lagged study

Previous studies have shown that a high prevalence of depression and anxiety is a key factor leading to a decrease in student satisfaction with university life. Therefore, this study used two waves of longitudinal data to investigate the longitudinal relationships among depression, anxiety, and student satisfaction with university life among college students. We employed correlation analysis and cross-lagged models to analyze the correlation and cross-lagged relationships among depression, anxiety, and student satisfaction with university life. The results indicate a significant negative correlation between depression and student satisfaction with university life. The cross-lagged models indicate that depression (Time 1) negatively predicts student satisfaction with university life (Time 2). Anxiety (Time 1) does not have a significant predictive effect on student satisfaction with university life (Time 2). Moreover, student satisfaction with university life negatively predicts both depression (Time 2) and anxiety (Time 2). Improving student satisfaction with university life has a significant impact on reducing levels of depression and anxiety among college students. The research results can provide valuable information for mental health professionals, school administrators, and policymakers, enabling them to take more targeted measures to reduce depression and anxiety symptoms among university students and enhance student satisfaction with university life.

The Prevalence of Anxiety Among University Students in the United Arab Emirates Following the COVID-19 Lockdown

INTRODUCTION

The COVID-19 pandemic, with its consequential lifestyle changes, is anticipated to contribute to increased anxiety levels, particularly among university students who already contend with significant academic stress. We aim to assess the prevalence of anxiety among university students in the United Arab Emirates (UAE) following the COVID-19 lockdown period.

METHODS

We conducted a descriptive cross-sectional study among students enrolled in UAE universities. A self-administered questionnaire was utilized to gather demographic data, assess anxiety levels using the generalized anxiety disorder-7 scale, explore potential factors associated with heightened anxiety, investigate the impact of increased anxiety on academic performance, and identify coping mechanisms employed post-lockdown.

RESULTS

Of the 369 participating students, anxiety levels were minimal in 87 (23.6%), mild in 163 (44.2%), and moderate to severe in 119 (32.2%) subjects. Moreover, increased anxiety levels were significantly correlated with poor/fair sleep quality (p=0.002). Importantly, students with moderate to severe anxiety levels exhibited poorer performance in exams and assignments (p=0.001) and encountered difficulties in maintaining focus on studies (p<0.001). The predominant coping mechanisms employed by students included self-distraction, prayer, and maintaining a positive attitude.

CONCLUSION

The majority of students in our study experienced mild to severe levels of anxiety following the COVID-19 lockdown period. We hope that our findings will prompt university and government officials to implement effective screening and preventive strategies to adequately support university students in future public health crises.

Anxiety and depression as predictors of life satisfaction during pre-professional health internships in COVID-19 times: the mediating role of psychological well-being

Due to the emotional impact of COVID-19 on university students, the goal was to explore the relationship between anxiety, depression, psychological well-being, and life satisfaction among pre-professional interns. The research was carried out using an explanatory cross-sectional design, with the participation of 1011 pre-professional interns of 13 health networks from the department of Puno (Peru). Data were collected using the Satisfaction with Life Scale, Generalized Anxiety Disorder Scale-2, Patient Health Questionnaire 2, and the Psychological Wellbeing Scale. The main data analysis was carried out using the R statistical software, and implementing the confirmatory factor analysis technique, which evidenced that the explanatory model provides an acceptable value. Based on the above, a negative relationship between depression and life satisfaction, (β = -.60, p < .001) and a positive relationship between anxiety and life satisfaction (β = .28, p < .001) was shown, in addition to a mediating effect of the psychological wellbeing related to depression and life satisfaction (p < .001). In conclusion, life satisfaction is explained concerning the degree of depression and anxiety, as well as the moderating effect of psychological well-being. Despite that, there is an urgent need to take preventive actions to strengthen the mental health of the pre-professional health interns, who have also been providing support during the COVID-19 pandemic.

First-Year College Students' Mental Health in the Post-COVID-19 Era in Guangxi, China: A Study Demands-Resources Model Perspective

The post-COVID-19 era means that the COVID-19 is basically under control; however, the risk of the pandemic still affects people's work, study, and life, physically and psychologically. In this era, due to the more challenges first-year college students face, more attention should be paid to their mental health. An emerging study demands-resources (SD-R) model can explain the influencing mechanism of college students' mental health. This model suggests that study demands increase the risk of student burnout, which results in mental health problems; meanwhile, study resources reduce student burnout and increase student engagement, thus improving mental health. Based on the SD-R model, this study explores the impacts of time pressure, emotional exhaustion, perceived social support, and student engagement on mental health and provides adequate measures to reduce the risk of mental health problems among first-year students. Time pressure, perceived social support, emotional exhaustion, student engagement, and mental health scales were used to investigate 537 first-year students at three universities in Guangxi, China, of whom 290 (54%) were female, and 247 (46%) were male, and the average age was 18.97 ± 1.01. Results indicated that: (1) Moderate scores on time pressure and emotional exhaustion and slightly-above-the-median scores on perceived social support, student engagement, and mental health were found among first-year students in the post-COVID-19 era. (2) Time pressure had a positive relationship with emotional exhaustion and a negative relationship with mental health. (3) Perceived social support was negatively correlated with emotional exhaustion but positively correlated with student engagement, and thus improved mental health. Results of this study with a sample of first-year college students in China support the hypotheses based on the SD-R model. These findings suggest that increasing perceived social support and student engagement while decreasing time pressure and emotional exhaustion may promote mental health among first-year college students.