Domestic Violence and Mental Health During the COVID-19 Pandemic in Bangladesh
Bibliographic record
Abstract
BACKGROUND: The COVID-19 lockdown, the advent of working from home, and other unprecedent events have resulted in multilayer and multidimensional impacts on our personal, social, and occupational lives. Mental health conditions are deteriorating, financial crises are increasing in prevalence, and the need to stay at home has resulted in the increased prevalence of domestic violence. In Bangladesh, where domestic violence is already prevalent, the lockdown period and stay-at-home orders could result in more opportunities and increased scope for perpetrators of domestic violence. OBJECTIVE: In this study, we aimed to determine the prevalence and pattern of domestic violence during the initial COVID-19 lockdown period in Bangladesh and the perceptions of domestic violence survivors with regard to mental health care. METHODS: We conducted this cross-sectional web-based study among the Bangladeshi population and used a semistructured self-reported questionnaire to understand the patterns of domestic violence and perceptions on mental health care from August to September 2020. The questionnaire was disseminated on different organizational websites and social media pages (ie, those of organizations that provide mental health and domestic violence services). Data were analyzed by using IBM SPSS (version 22.0; IBM Corporation). RESULTS: We found that 36.8% (50/136) of respondents had faced domestic violence at some point in their lives; psychological abuse was the most common type of violence. However, the prevalence of the economical abuse domestic violence type increased after the COVID-19 lockdown was enforced. Although 96.3% (102/136) of the participants believed that domestic violence survivors need mental health support, only 25% (34/136) of the respondents had an idea about the mental health services that are available for domestic violence survivors in Bangladesh and how and where they could avail mental health services. CONCLUSIONS: Domestic violence is one of the most well-known stressors that have direct impacts on physical and mental health. However, the burden of domestic violence is often underreported, and its impact on mental health is neglected in Bangladesh. The burden of this problem has increased during the COVID-19 crisis, and the cry for mental health support is obvious in the country. However, it is necessary to provide information about available support services; telepsychiatry can be good option for providing immediate mental health support in a convenient and cost-effective manner.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.002 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".