Gender-based Violence (GBV) on Bangladeshi Women and Girls during COVID-19 in Toronto: Forms, Causes, and Mental Health Impacts
Bibliographic record
Abstract
Aims: The COVID-19 pandemic lockdown has led to a rise in the number of gender-based violence (GBV) cases within families. This increase is mainly due to patriarchal influence, prolonged stay at home, social isolation, and financial abuse. Women and girls, in particular, have been adversely affected by this phenomenon, experiencing significant mental health impacts. A study conducted in Toronto explored the various types and causes of violence against South Asian Bangladeshi women and girls and how it affects their mental health, too. Methodology: Bangladeshi-Canadian Community Services (BCS), an ethnic organization conducted a study on gender-based violence (GBV) from Sep to Dec 2021. Two extensive Zoom group discussions involved 55 females and six males, including social workers (n=9), community leaders/workers (n=17), community youth ambassadors (n=9), and community members (n=26, among them n=11 were victims). Audio recordings were transcribed and translated into English, then thematically coded to identify forms of GBV and provide narratives based on participants' experiences. Results: South Asian Bangladeshi women and girls have been subjected to various forms of violence by their husbands or male family members, such as physical, psychological/emotional, verbal, and financial violence. Physical violence includes shaking their wives' hands and necks and pushing wives and girls. Psychological, verbal, and financial violence includes scolding, threatening, demoralizing, blaming, belittling, coercing, stealing money, and committing financial fraud. The causes of violence against women during the lockdown are related to the loss of husbands' jobs and idleness, the tendency to establish male power over women out of fear of losing patriarchal control during a crisis, husbands' mental turmoil due to the lockdown and financial crisis, women's protest against extramarital relationships, fear of contracting viruses, taking advantage of women's vulnerability, and greediness towards wives' money. As a result of this violence, many victims have reported experiencing anxiety, mental trauma, frustration, distress, depression, and discrimination. Victims of violence have faced many obstacles in accessing appropriate providers, such as language barriers and threats from their husbands. Conclusions: The COVID-19 pandemic has disproportionately affected South Asian Bangladeshi women and girls who are experiencing gender-based violence. To address this issue, policymakers, governments, and ethnic organizations must collaborate to create comprehensive plans that aim to eliminate gender-based violence, as well as associated cultural problems and stigma. Community organizations can provide resources and support for victims, working with families and husbands to create a safer environment for those affected.
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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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".