Sleep duration during the COVID-19 pandemic in Bangladesh: A GIS-based large sample survey study
Bibliographic record
Abstract
Although several studies have been conducted in Bangladesh regarding sleep problems during the COVID-19 pandemic, none have utilized a large nationwide sample or presented their findings based on nationwide geographical distribution. Therefore, the aim of the present study was to explore the total sleep duration, night-time sleep, and daily naptime and their associated factors as well as geographic information system (GIS) distribution. A cross-sectional survey was carried out among 9730 people in April 2020, including questions relating to socio-demographic variables, behavioral and health factors, lockdown, depression, suicidal ideation, night sleep duration, and naptime duration. Descriptive and inferential statistics, both linear and multivariate regression, and spatial distribution were performed using Microsoft Excel, SPSS, Stata, and ArcGIS software. The results indicated that 64.7% reported sleeping 7-9 h a night, while 29.6% slept less than 7 h nightly, and 5.7% slept more than 9 h nightly. 43.7% reported 30-60 min of daily nap duration, whereas 20.9% napped for more than 1 h daily. Significant predictors of total daily sleep duration were being aged 18-25 years, being unemployed, being married, self-isolating 4 days or more, economic hardship, and depression. For nap duration, being aged 18-25 years, retired, a smoker, and a social media user were at relatively higher risk. The GIS distribution showed that regional division areas with high COVID-19 exposure had higher rates of non-normal sleep duration. Sleep duration showed a regional heterogeneity across the regional divisions of the country that exhibited significant associations with a multitude of socioeconomic and health factors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".