The effects of anxiety during COVID-19 on psychologicalexhaustion and social participation in college students
Bibliographic record
Abstract
Background.The continuous psychological decline caused by COVID-19 has become a serious social problem.Notably, a new word, "Corona Blue", has been coined by combining "Corona" and "blue", which symbolises depression, to describe the psychological difficulties people are facing.Objectives.This study aimed to confirm the effect of anxiety experienced during the coronavirus (COVID-19) pandemic on the psychological decline and social participation levels of college students.Material and methods.A questionnaire was provided to 130 university students aged 20-29 years.The questionnaire was prepared using the Coronavirus Anxiety Scale, Athens Insomnia Scale, Patient Health Questionnaire-9, Perceived Stress Scale and Maastricht Social Participation Profile to measure COVID-19 anxiety, sleep status, depression, stress and social participation levels.The survey was conducted online from June to July 2021 when the spread of COVID-19 in South Korea was continuous and social distancing was implemented as a government guideline.Results.COVID-19 anxiety correlated with sleep, depression and stress.However, social participation levels were not correlated with COVID-19 anxiety.A comparison of the psychological exhaustion variables (sleep, depression and stress) between the potential risk group and the normal group confirmed a statistically significant difference for sleep, depression and stress; however, the difference in social participation variables was not statistically significant.Conclusions.In addition, the public also needs to find ways to cope with psychological atrophy in preparation for a prolonged COVID-19 pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".