Change in prevalence over time and factors associated with depression among Bangladeshi older adults during the<scp>COVID</scp>‐19 pandemic
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Globally, the COVID-19 pandemic seriously affected both physical and mental health conditions. This study aims to assess changes in the prevalence of depression among older adults during the COVID-19 pandemic in Bangladesh and explore the correlates of depression in pooled data. METHODS: This study followed a repeated cross-sectional design and was conducted through telephone interviews on two successive occasions during the COVID-19 pandemic (October 2020 and September 2021) among 2077 (1032 in 2020-survey and 1045 in 2021-survey) older Bangladeshi adults aged 60 years and above. Depression was measured using the 15-item Geriatric Depression Scale (GDS-15). The binary logistic regression model was used to identify the factors associated with depression in pooled data. RESULTS: A significant increase in the prevalence of depression was noted in the 2021 survey compared to the 2020 survey (47.2% versus 40.3%; adjusted odds ratio (aOR): 1.40, 95% confidence interval (CI): 1.11-1.75). Depression was significantly higher among participants without a partner (aOR 1.92, 95% CI 1.45-2.53), with a monthly family income of <5000 BDT (aOR: 2.65, 95% CI 1.82-3.86) or 5000-10 000 BDT (aOR: 1.30, 95% CI 1.03-1.65), living alone (aOR 2.24, 95% CI 1.40-3.61), feeling isolated (aOR 3.15, 95% CI 2.49-3.98), with poor memory/concentration (aOR 2.02, 95% CI 1.58-2.57), with non-communicable chronic conditions (aOR 1.34, 95% CI 1.06-1.69), overwhelmed by COVID-19 (aOR 1.54, 95% CI 1.18-2.00), having difficulty earning (aOR 1.49, 95% CI 1.15-1.92) or obtaining food (aOR 1.56, 95% CI 1.17-2.09) during COVID-19 pandemic, communicating less frequently (aOR 1.35, 95% CI 1.07-1.70) and needing extra care (aOR 2.28, 95% CI 1.75-2.96) during the pandemic. CONCLUSIONS: Policymakers and public health practitioners should provide immediate mental health support initiatives for this vulnerable population during the COVID-19 pandemic and beyond. Policymakers should also invest in creating safe places to practise mindful eating, exercise, or other refuelling activities as a means of preventing and managing depression.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 it