A longitudinal study on depression and anxiety in college students during the first 106-days of the lengthy Argentinean quarantine for the COVID-19 pandemic

The development and evolution of the research topic on the mental health of college students: A bibliometric review based on CiteSpace and VOSviewer

Background

With the advances in society and in response to changing times, college students have had to face multiple challenges. These challenges frequently affect the mental health of college students, leading to significant consequences for their social lives, personal well-being, and academic achievements, thereby attracting extensive societal attention. Therefore, examining the current status of research topics related to the mental health of college students can assist academia in dissecting the influencing factors and seeking solutions at their source or through early intervention. This can contribute to a better understanding of and effectively address this challenge.

Method

CiteSpace and VOSviewer were used to conduct a bibliometric analysis of 1609 journal articles indexed in the Web of Science (WoS) database over the past two decades (2000-2022), which helped identify the current state of research and hot topics in the field based on development trends. Furthermore, this study analyzes and discusses the core authors, high-productivity countries and organizations, key journals, and keyword clustering in this field. This study clarifies the current research landscape, analyzes evolving trends based on developmental trajectories, and identifies forefront research hotspots. This study provides scholars with reference research directions and ideas for conducting subsequent studies.

Results

Since the beginning of the 21st century, research on college students' mental health has increased, especially in the past three years, and due to the impact of the COVID-19 pandemic and online distance learning, the number of publications has increased rapidly. With the increase in attention and publication volume, the countries and organizations contributing papers as well as core journals have all started to take shape. Cluster and evolution analyses found that several stable research topics have been formed in this research field, and many new and diverse topics are continuously emerging with time.

Conclusion

and prospect: The findings prove that the field of college students' mental health has begun to take shape, gradually shifting from conceptual research to the implementation of specific interventions. However, whether specific interventions are effective and how effective they are require further investigation.

Machine learning models predict the emergence of depression in Argentinean college students during periods of COVID-19 quarantine

Introduction

The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need.

Aims

1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction.

Methods

A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models' performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors.

Results

The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features.

Discussion

These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.

Families, Schools, and the Longitudinal Changes in Psychological Distress among College Students during the First Year of the COVID-19 Pandemic: Evidence from a National Panel Survey in China

BACKGROUND

The psychological well-being of students in higher education has become an increasingly important concern in the context of the COVID-19 pandemic. The majority of prior studies were cross-sectional and thereby failed to capture the individual changes in mental health over time. Moreover, few studies have explored whether and how socioeconomic and education backgrounds could be related to college students' mental health changes. This study aimed to fill these gaps.

METHODS

Data were from a nationwide, two-wave panel survey of college students in China. Baseline information was collected in November 2019, i.e., before the outbreak of the COVID-19 epidemic in China. A follow-up survey was conducted in November and December 2020, when the epidemic was effectively contained in mainland China. In both waves, mental health was assessed with the 10-item Kessler Psychological Distress Scale (K10). The between-wave changes in psychological distress were measured and categorized.

RESULTS

Overall, 13.5% of students experienced at least a one-standard-deviation increase in psychological distress over the one-year follow-up. Results from both bivariate and multivariable analysis showed that this marked distress increase was more pronounced among vocational college students (vs. academic undergraduate and postgraduate students) and those with lower levels of academic performance. In contrast, family socioeconomic status (as measured by parental education, family economic condition, and current residence) was not associated with distress changes over time.

CONCLUSIONS

These findings highlight the importance of the educational disparities within the higher education system in understanding the mental health dynamics of college students in the context of the COVID-19 pandemic.

The COLIBAS Study—COVID-19 Lockdown Effects on Mood, Academic Functioning, Alcohol Consumption, and Perceived Immune Fitness: Data from Buenos Aires University Students

A recent study was conducted in the Netherlands to evaluate the impact of the 2019 coronavirus (COVID-19) pandemic and its associated lockdown periods on academic functioning, mood, and health correlates such as alcohol consumption. The study revealed that lockdowns were associated with a significantly poorer mood and a reduced perceived immune fitness. Overall, a reduction was seen in alcohol consumption during the lockdown periods. Academic functioning in terms of performance was unaffected; however, a significant reduction in interactions with other students and teachers was reported. There was, however, great variability between students as follows: both an increase and a reduction in alcohol consumption were reported, as well as improvements and poorer academic functioning. The aim of the current online study was to replicate these findings in Argentina. To this extent, a modified version of the survey was conducted among students at the University of Buenos Aires, which was adapted to the local lockdown measures. The survey assessed possible changes in self-reported academic functioning, mood, and health correlates, such as alcohol consumption, perceived immune functioning, and sleep quality compared to before the COVID-19 pandemic. Retrospective assessments were made for four periods, including (1) the period before COVID-19, (2) the first lockdown period (March–December 2020), (3) summer 2021 (January-March 2021, no lockdown), and (4) the second lockdown (from April 2021 to July 2021). This article describes the content of the survey and the corresponding dataset. The survey was completed by 508 participants.