Mental Health Impact of Gender-Based Violence Amid COVID-19 Pandemic: A Review
Bibliographic record
Abstract
Gender-based violence (GBV) and poor mental health have received particular attention among healthcare professionals, policymakers, and researchers amid the COVID-19 pandemic. This paper presents a review of available literature to understand the dynamics of GBV and its mental health impact in the context of COVID-19. Confinement and control by abusive partners, social and economic disruption, and restricted access to healthcare services were identified as the main contributing factors of GBV. The paper elaborates on the contribution of broader socioeconomic determinants of health as well as cultural and societal factors of victimization in shaping GBV by placing specific populations or individuals in a more vulnerable position within the society based on their gender. Socioeconomic determinants included socioeconomic status, education, migration and racial, ethnic, or gender-based minoritisation. Cultural and societal factors of victimization are mostly related to gender-based structural power discrepancies and communication patterns. Evidence suggests a complex relationship between COVID-19 specific stressors, such as health anxiety and intolerance of uncertainty, GBV, and mental health issues. COVID-19 stressors might directly trigger the mechanism of aggression and cause physical or psychological violence and associated mental health implications in victims, or it might be mediated by pre-existing mental health issues experienced by perpetrators. Bangladesh Journal of Medical Science Vol.20(5) 2021 p.17-25
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".