Prevalence of Sleep Disturbances During COVID-19 Pandemic in a Nepalese Population: A Cross-Sectional Study
Bibliographic record
Abstract
Objectives: The coronavirus disease (COVID-19) pandemic and news of daily increasing cases inside Nepal and worldwide is adding to the fear that leads to anger, anxiety, frustration, and stress, emotions that directly affect sleep quality. This study aimed to assess sleep disturbances during the COVID-19 pandemic in a Nepalese population.Methods: This cross-sectional study recruited 206 Nepali residents who completed anonymous self-administered questionnaires. The Insomnia Severity Index (ISI) questionnaire was used to measure sleep disturbances before and after the COVID-19 pandemic. The gathered data were analyzed using descriptive statistics and inferential statistics using SPSS version 20 statistical software.Results: There was a significant variation in sleep disturbances among Nepalese residents before versus after the COVID-19 pandemic (p<0.001). The prevalence of clinical moderate insomnia has increased tremendously in Nepalese individuals. Before the pandemic’s onset, only 3.9% of the participants had moderate to severe levels of clinical insomnia; after its onset, this value increased to 17.5%. The mean ISI scores were 6.35±4.65 and 8.01±6.01 before and after the pandemic’s onset, respectively.Conclusions: Our study findings suggest that people are suffering tremendously with sleep disturbances and calls for further research and active measures to help increase sleep quality during the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".