Impact of COVID-19 anxiety on loneliness and sleep quality of students and professionals in Bangladesh
Bibliographic record
Abstract
The COVID-19 pandemic has globally affected almost every aspect of people's lives, especially, their physical and mental well-being. The degree of its impact, however, is different from place-to-place and person-to-person. Although there is a growing literature on the variable impact of the pandemic on the quality of sleep, loneliness, and mood across different populations (e.g., students, health-workers), little is known about how COVID-19-specific anxiety affects the loneliness feeling and sleep quality among students and employees, specifically, in a low-resource region like Bangladesh. The present study aimed to investigate the effect of COVID-related anxiety on the feeling of loneliness and sleep quality of students and professionals in Bangladesh. Additionally, we were interested in comparing the level of COVID-specific anxiety, loneliness, and quality of sleep between these two groups. In total, 211 Bangladeshi students and professionals participated in an online survey in August 2021 when the restriction was still in place. Measures of COVID-19 anxiety, loneliness, and sleep quality scales were used. Regression analysis indicated that overall loneliness and poor sleep quality were strongly predicted by COVID-specific anxiety regardless of being a student or professional. Almost half of the study population (48.3 %) felt severe loneliness and 70.01 % were bad sleepers. Mann-Whitney U test revealed that professionals felt more emotionally lonely, had a higher level of COVID-19-specific anxiety, and had poorer sleep quality than students. A better support structure should be implemented to help the population, particularly, the professionals to lessen their COVID-19-related anxiety and loneliness, and promote better sleep for alleviating stress and improved well-being.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".