Pandemic within a pandemic! Policy Implications of community-based Interventions to mitigate violence against women during COVID-19 in Urban Slums of Lucknow, India
Bibliographic record
Abstract
Background: The COVID-19 pandemic has brought an unprecedented adverse impact on women's health. Evidence from the literature suggests that violence against women has increased multifold. Gender-based violence in urban slums has worsened due to a lack of water and sanitation services, overcrowding, deteriorating conditions and a lack of institutional frameworks to address gender inequities. Methods: The SAMBHAV (Synchronized Action for Marginalized to Improve Behaviors and Vulnerabilities) initiative was launched between June 2020 to December 2020 by collaborating with the Uttar Pradesh state government, UNICEF and UNDP. The program intended to reach 6000 families in 30 UPS (Urban Poor settlements) of 13 city wards. These 30 UPS were divided into 5 clusters. The survey was conducted in 760 households, 397 taken from randomly selected 15 interventions and 363 households from 15 control UPS. This paper utilized data from a baseline assessment of gender and decision-making from a household survey conducted in the selected UPS during July 03-15, 2020. A sample size of 360 completed interviews was calculated for intervention and control areas to measure changes attributable to the SAMBHAV intervention in the behaviours and service utilization (pre- and post-intervention). Results: The data analysis showed a significant difference (p-value < 0.001) between respondents regarding women's freedom to move alone in the control and intervention area. It also reflected a significant difference between control and intervention areas as the respondents in the intervention area chose to work for the cause of gender-based violence. Conclusion: The SAMBHAV initiative brought an intersectional lens to gender issues. The community volunteers were trained to approach issues based on gender-based violence with the local public, and various conferences and meetings were organized to sensitize the community. The initiative's overall impact was that it built momentum around the issue of applying the concept of intersectionality for gender issues and building resilience in the community. There is still a need to bring multi-layered and more aggressive approaches to reduce the prevalence of gender-based violence in the community.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".