Psychological Impacts of COVID-19 on In-patients in a COVID-19 Dedicated Hospital in Bangladesh
Bibliographic record
Abstract
Background: COVID-19 is a global pandemic, and the World Health Organization has urged Southeast Asian countries to implement effective measures. However, countries like Bangladesh, with a poorly structured healthcare system and high population density, are struggling to meet these challenges. Objective: This study was aimed to assess the psychological impact of COVID-19 on patients at a COVID-19- dedicated hospital. Methodology: This cross-sectional study was conducted on 322 patients admitted with COVID-19 at Mugda Medical College and Hospital, a dedicated COVID-19 facility in Dhaka, Bangladesh, from May to June 2020. The Patient Health Questionnaire-9 (PHQ-9) was used to assess depression, the Generalized Anxiety Disorder-7 (GAD-7) scale measured the severity of self-reported anxiety, and the Insomnia Severity Index (ISI) was employed to diagnose self-reported insomnia. Results: This study assessed the psychological profiles of 382 patients. According to the PHQ-9 scale, 39.1% of patients had moderate depression, with 60.9% having severe depression. Anxiety levels were measured on the GAD-7 scale at 13.7% for moderate anxiety and 86.3% for severe anxiety. Based on the Insomnia Severity Index, 18.9% of patients suffered mild insomnia, 69.6% moderate insomnia, and 11.5% severe insomnia. Conclusion: The study has revealed that a significant proportion of COVID-19 patients experienced mental health disturbances during their hospitalization. It is essential to closely monitor their mental well-being and provide timely interventions. Journal of Current and Advance Medical Research, January 2024;11(1):28-33
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".