Mental health difficulties of adults with COVID-19-like symptoms in Bangladesh: A cross-sectional correlational study
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The rapid spread of novel corona virus disease (COVID-19) coupled with inefficient testing capacities in Bangladesh has resulted in a number of deaths from COVID-19-like symptoms that have no official test results. This study was the first study that explored the mental health of adults with the most common COVID-19-like symptoms in Bangladesh. METHODS: This cross-sectional correlational study gathered data via an online survey to explore the mental health of Bangladeshi adults with symptoms akin to COVID-19. Level of stress, anxiety symptoms, and depressive symptoms were measured with the DASS-21. Chi-square tests and multivariate logistic regression was performed to examine the association of variables. RESULTS: The prevalence rates of anxiety symptoms and depressive symptoms of the overall population were 26.9% and 52.0% respectively and 55.6% reported mild to extremely severe levels of stress. Multivariate logistic regression determined that respondents with COVID-19-like symptoms reported higher odds for stress level (AOR = 2.043, CI = 1.51 to 2.76), anxiety symptoms (AOR = 2.770, CI = 2.04 to 3.77) and depressive symptoms (AOR = 1.482, CI = 1.12 to 1.96) than asymptomatic respondents. LIMITATIONS: There was a chance of recall bias as it was not possible to validate the information due to the retrospective design of the study. Recruitment methods only captured internet users, which reduces the generalizability of findings. CONCLUSIONS: Patients with symptoms like those of COVID-19 should be prioritized in the healthcare setting in order to reduce mental health difficulties throughout the pandemic .
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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