Mental Health Status of Teachers During the Second Wave of the COVID-19 Pandemic: A Web-Based Study in Bangladesh
Bibliographic record
Abstract
Background: Following the outbreak of the COVID-19 pandemic, the government of Bangladesh implemented strict non-therapeutic measures, i.e., "social distancing," "lockdown," "work from home," in the first quarter of 2020. Like other professionals, teachers at schools, colleges and universities were confined within households. However, the introduction of online education imposed an additional burden on teachers along with growing household responsibilities, thus, affecting their psychological state. Aims: This study was aimed to explore the prevalence of mental health problems among teachers in Bangladesh and to identify the associated risk factors. Methods: This web-based cross-sectional study was conducted during the second wave of COVID-19 pandemic in Bangladesh. Data were collected from 381 teachers working at schools, colleges, and universities between 01 August and 29 August 2021 by administering a self-reported e-questionnaire using Google Form, where the mental health of teachers was assessed by depression, anxiety, and stress scale. Data were analyzed using IBM SPSS Statistics (Version 26) and STATA Version 16, and multiple linear regression was executed to predict mental health problems among teachers. Results: The findings indicate that the overall prevalence of depression, anxiety, and stress among teachers was 35.4%, 43.7%, and 6.6%, respectively. The prevalence was higher among male and older teachers than among their female and younger colleagues. The findings further showed that place of residence, institution, self-reported health, usage of social and electronic media, and fear of COVID-19 significantly influenced the mental health status of teachers. Conclusion: It is strongly recommended that the government and policymakers provide proper mental health services to teachers in order to reduce mental health problems and thus sustain the quality of education during and after the pandemic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".