Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study
Bibliographic record
Abstract
Background: Frontline doctors are the most vulnerable and high-risk population to get the novel coronavirus disease 2019 (COVID-19) infection. Hence, we aimed to evaluate the anxiety, depression, sleep disturbance and fear of COVID-19 among frontline doctors of Bangladesh during the pandemic, and the associated factors for these psychological symptoms. Methods: In total, 370 frontline doctors who were involved in the treatment of suspected or confirmed COVID-19 patients during the pandemic took part in an online cross-sectional study. Recruitment was completed using convenience sampling and the data were collected after the start of community transmission of COVID-19 in the country. Anxiety and depression, sleep disturbance, and fear of COVID-19 were assessed by the Patient Health Questionnaire-4, two-item version of the Sleep Condition Indicator, and the Fear of Coronavirus-19 scale, respectively. Socio-demographic information, health service-related information, co-morbidity, and smoking history were collected for evaluating risk factors. The proportion of psychological symptoms were presented using descriptive statistics and the associated factors were identified using multinomial logistic regression analysis. Results: Of the doctors, 36.5% had anxiety, 38.4% had depression, 18.6% had insomnia, and 31.9% had fear of COVID-19. In multinomial logistic regression, inadequate resources in the workplace were found as the single most significant predictor for all psychological outcomes: anxiety and/or depression (severe, OR 3.0, p=0.01; moderate, OR 5.3, p=0.000; mild, OR 2.3, p=0.003), sleep disturbance (moderate, OR 1.9, p=0.02), and fear of COVID-19 (severe, OR 1.9, p=0.03; moderate, OR 1.8, p=0.03). Conclusions: The study demonstrated a high burden of psychological symptoms among frontline doctors of Bangladesh during the COVID-19 pandemic situation. Inadequate resources are contributing to the poor mental health of Bangladeshi doctors. The supply of sufficient resources in workplaces and mental health counseling may help to mitigate the burden of the psychological symptoms identified among the respondents..
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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".