Prevalence and correlates of total sleep time among the older adults during COVID-19 pandemic in Bangladesh
Bibliographic record
Abstract
Purpose: The present study was aimed to identify inappropriate sleep duration and its correlates among the Bangladeshi older adults during the COVID-19 pandemic. Material and methods: This cross-sectional study was carried out among 1030 older adults aged 60 years and above in Bangladesh. Information was collected through telephone interviews using a pretested semi-structures questionnaire installed in SurveyCTO mobile app. Sleep duration was defined as total sleep time (TST) in last 24 h including day and nighttime sleep. TST was further categorized into shorter (<7 h), recommended (7-8 h), and longer sleep (>8 h) according to 2015 National Sleep Foundation guideline. The multinomial logistic regression model identified the factors associated with sleep duration. Results: Mean TST was 7.9 h (SD=1.62). Of the total participants, 28.2% had longer and 17.8% shorter sleep duration. In the regression model, participants' age of ≥80 years (OR: 3.36, 1.46-7.73), monthly family income of <5,000 Bangladeshi Taka (OR: 3.50, 1.79-6.82), difficulty in getting medicine during COVID-19 (OR: 1.72, 1.05-2.82), lack of communication during the pandemic (OR: 2.20, 1.43-3.40) and receiving COVID-19 related information from friends/family/neighbours (OR: 1.83, 1.11-3.01) were significantly associated with shorter TST. On the other hand, monthly family income of < 5,000 Bangladeshi Taka (OR: 2.00, 1.13-3.53), difficulty in getting medicine during COVID-19 pandemic (OR: 2.01, 1.33-3.03) and receiving COVID-19 related information from radio/TV (OR: 2.09, 1.22-3.59) were associated with longer TST. Conclusions: The study findings suggest implementing sleep management program for older adults in Bangladesh, particularly during emergencies like COVID-19.
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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 | 0.001 |
| 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 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".