Are There Long‐term Consequences to Psychological Stress During a Medical Event?

Chang, Bernard P.

doi:10.1111/acem.13878

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Serious public health emergencies are major stressors in society, 1 which may cause mental problems such as acute stress disorder, post-traumatic stress disorder, depression, and anxiety. 2 The novel coronavirus disease 2019 (COVID- 19) pandemic, which initially appeared in Wuhan, Hubei Province, China at the end of 2019, has been an unexpected, largescale, public health event that disrupted communities and caused death, destruction and trauma, and aroused widespread concern of the world.…”

Section: Introductionmentioning

confidence: 99%

Correlation Between Chronotypes and Depressive Symptoms Mediated by Sleep Quality Among Chinese College Students During the COVID-19 Pandemic

Zhang¹,

Jiao²,

Guan³

et al. 2023

NSS

View full text Add to dashboard Cite

Purpose The COVID-19 pandemic has adversely impacted the mental health of the population. The current study aimed to determine the prevalence of depressive symptoms and sleep disturbances among Chinese college students during the COVID-19 pandemic and investigate the correlations between chronotypes, sleep quality, and depressive symptoms. Participants and Methods In the current study, 2526 college students responded anonymously to an online questionnaire survey from 26 May 2020 to 20 July 2020. The participants’ chronotypes, sleep quality, and depressive symptoms were evaluated using the Chinese version of the Morning and Evening Questionnaire-5 (MEQ-5), Pittsburgh Sleep Quality Index (PSQI), and Patient Health Questionnaire-9 (PHQ-9). Sociodemographic information of the participants was also acquired. Statistical analyses were performed using Statistical Package for Social Sciences (SPSS) 19.0 software, with the mediating effect assessed by Hayes’ PROCESS Macro. Results During the COVID-19 pandemic, the prevalence of depressive symptoms and sleep disturbances among Chinese college students surveyed was 54.95% and 48.18%, respectively. From absolute evening chronotype to absolute morning chronotype, the surveyed college students’ chronotypes were negatively correlated with their depressive symptoms. Moreover, the mediation analysis showed that the correlation between chronotypes and depressive symptoms was fully mediated by sleep quality. Eveningness college students with poorer sleep quality were more likely to report higher levels of depressive symptoms. Conclusion Our findings suggest that during the COVID-19 pandemic, delayed circadian preference (ie, eveningness) may be linked to worse depressive symptoms among Chinese college students, and call for more attention to the sleep quality of Chinese college students as sleep quality fully mediated the correlation between chronotypes and depressive symptoms among them. Reasonable adjustment in bedtime/circadian preference and improvement in sleep quality may help to reduce the prevalence and severity of depressive symptoms among Chinese college students.

show abstract

Section: Introductionmentioning

confidence: 99%

Correlation Between Chronotypes and Depressive Symptoms Mediated by Sleep Quality Among Chinese College Students During the COVID-19 Pandemic

Zhang¹,

Jiao²,

Guan³

et al. 2023

NSS

View full text Add to dashboard Cite

show abstract

A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era

Zhang,

Zhong,

Mao

et al. 2024

Electronics

View full text Add to dashboard Cite

With the rapid development of artificial intelligence in recent years, intelligent evaluation of college students’ growth by means of the monitoring data from training processes is becoming a promising technique in the field intelligent education. Current studies, however, tend to utilize course grades, which are objective, to predict students’ grade-point averages (GPAs), but usually neglect subjective factors like psychological resilience. To solve this problem, this paper takes mechanical engineering as the research object, and proposes a new machine-learning-driven GPA prediction approach to evaluate the academic performance of engineering students by incorporating psychological evaluation data into basic course scores. Specifically, this paper adopts SCL-90 psychological assessment data collected in the freshman year, including key mental health indicators such as somatization, depression, hostility, and interpersonal sensitivity indicators, as well as professional basic course scores, including mechanical principles, mechanical design, advanced mathematics, and engineering drawing. Four representative machine learning algorithms, Support Vector Machine (SVM), CNN-CBAM, Extreme Gradient Boosting (XGBoost) and Classification and Regression Tree (CART) that include deep and shallow models, respectively, are then employed to build a classification model for GPA prediction. This paper designs a validation experiment by tracking 229 students from the 2020 class from the School of Mechanical and Electrical Engineering of Henan University of Science and Technology, China. The students’ academic performance in senior grades is divided into five classes to use as the prediction labels. It is verified that psychological data and course data can be effectively integrated into GPA prediction for college students, with an accuracy rate of 83.64%. Meanwhile, this paper also reveals that anxiety indicators in the psychological assessment data have the greatest impact on college students’ academic performance, followed by interpersonal sensitivity. The experimental results also show that, for predicting junior year GPAs, psychological factors play more important role than they do in predicting sophomore GPAs. Suggestions are therefore given: the current practice in existing undergraduate teaching, i.e., only conducting psychological assessments in the initial freshman year, should be updated by introducing follow-up psychological assessments in each academic year.

show abstract

Are There Long‐term Consequences to Psychological Stress During a Medical Event?

Cited by 2 publications

References 11 publications

Correlation Between Chronotypes and Depressive Symptoms Mediated by Sleep Quality Among Chinese College Students During the COVID-19 Pandemic

Correlation Between Chronotypes and Depressive Symptoms Mediated by Sleep Quality Among Chinese College Students During the COVID-19 Pandemic

A New Machine-Learning-Driven Grade-Point Average Prediction Approach for College Students Incorporating Psychological Evaluations in the Post-COVID-19 Era

Contact Info

Product

Resources

About