literature review for gender based violence

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• Published: 28 August 2021

Risk and protective factors for GBV among women and girls living in humanitarian setting: systematic review protocol

• Maureen Murphy   ORCID: orcid.org/0000-0002-3507-7564 1 ,
• Mary Ellsberg 1 ,
• Aminat Balogun 1 &
• Claudia Garcia-Moreno 2  

Systematic Reviews volume  10 , Article number:  238 ( 2021 ) Cite this article

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While one in three women around the world are estimated to have experienced intimate partner or sexual violence, these rates are often exacerbated during conflict and humanitarian crisis. This systematic review seeks to provide an overview of existing research on risk and protective factors associated with gender-based violence (GBV) in conflict and humanitarian settings.

Studies will be searched from the following databases: PubMed (Medline); PsycINFO; Scopus; Global Health; and Cochrane Center trials registrar. In addition, targeted searches of the internet repositories for GBV will be conducted. We will include studies that are published between January 1995 and December 2020 and document risk or protective factors for gender-based violence against women and girls in conflict or humanitarian settings. Two reviewers will independently screen and extract data for the review, with a third reviewer arbitrating disputes and ensuring quality. A quality assessment of the included studies will be undertaken using a modified GRADE system. Narrative synthesis will be utilized to analyze the data.

The results of this study will inform the design and delivery of GBV prevention programs in conflict and humanitarian settings as well as contribute to the attainment of Sustainable Development Goal 5. The results will be published in a peer-reviewed journal and will be utilized at the World Health Organization to inform efforts to prevent GBV in conflict and humanitarian settings.

Systematic review registration

The protocol has been registered with PROSEPERO ( CRD42020198695 ).

Peer Review reports

While one in three women around the world are estimated to have experienced intimate partner violence or non-partner sexual violence, two of the most common forms of gender-based violence (GBV), evidence shows that the risk of GBV is often higher in conflict and humanitarian crisis [ 1 ]. While rates of sexual violence vary by context, overall an estimated 21.4% of refugee and displaced persons in complex humanitarian emergencies have experienced such violence [ 2 ]. Even more women and girls experience intimate partner violence (IPV) compared to sexual violence alone and, in some conflict-affected contexts, rates of IPV as high as 73% of ever partnered women have been documented [ 3 , 4 ]. Furthermore, exposure to armed conflict has been found to be associated with higher rates of IPV, suggesting that conflict and humanitarian crises directly and indirectly affect the drivers of multiple forms of GBV [ 4 , 5 ].

There is still limited evidence on the scope and magnitude of non-partner sexual violence and IPV in conflict and humanitarian settings, and even less is known about other forms of GBV. For example, a systematic review of child, early and forced marriages in these settings found rates that ranged from 3 to 51% and no assessment was made about how armed conflict affected marriage rates [ 6 ]. The same study also examined rates of sexual exploitation and found there was not enough data to estimate prevalence. Similarly, other forms of GBV such as harmful practices (e.g., female genital mutilation, wife inheritance), trafficking, and femicide have not been consistently documented in conflict and humanitarian settings. Despite this, the limited research available has suggested that conflict and humanitarian settings can reinforce and potentially increase some of these practices. For example, adolescent girls in conflict settings may be married at early ages due to poverty or a desire to protect their virginity in contexts where conflict-related sexual violence is rampant [ 7 ].

No matter the context, there is agreement that the root cause of GBV is patriarchal gender norms and inequitable power dynamics and research has demonstrated the association between unequal gender norms and increased rates of IPV [ 8 ]. In conflict and humanitarian settings, programmers and academics believe that women and girls may be at heightened risk of violence for a variety of reasons including: displacement, the breakdown of social structures, a lack of law enforcement, the potential further entrenchment of harmful gender norms, and the loss of livelihood opportunities for both men and women in the community, among other reasons. In addition, active conflict dynamics (including the use of rape as a war tactic, breakdown of control of armed forces, increased availability of weapons, etc.) may also influence rates of GBV during these periods.

Since the 1995 United Nations Fourth World Conference on Women, there has been a considerable growth in research on GBV. Global initiatives such as the World Health Organization’s Multi-Country Study on Domestic Violence have standardized data collection measures and developed rigorous comparable evidence on prevalence and risk factors for GBV. In recent years, this increase in attention and rigor for GBV research has begun to expand into conflict and humanitarian crisis settings. Researchers have demonstrated it is possible to conduct rigorous prevalence studies [ 4 ] and impact evaluations [ 9 , 10 , 11 ] in these settings.

Despite the increasing evidence-base, gaps remain in our understanding of how conflict and other humanitarian crises impact rates of GBV. While researchers have conducted reviews of prevalence [ 2 , 3 , 6 ] and interventions [ 12 , 13 , 14 , 15 , 16 ], no systematic reviews have been conducted that specifically examine risk and protective factors that may influence GBV rates in these settings. This systematic review seeks to close this gap and provide an overview of existing research on risk and protective factors associated with GBV in conflict and other humanitarian settings. The findings of this review will inform practitioners and assist the field to develop more evidence-based prevention programming in conflict and humanitarian settings. In addition, the findings will be used to inform the World Health Organization to develop a GBV prevention framework for humanitarian settings.

Methods/design

The findings of this systematic review will be reported in line with recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols (PRISMA) statement [ 17 ].

Review questions

The review questions that will be explored are the following:

What are risk factors for experiencing gender-based violence for women and girls living in humanitarian settings (conflict-affected, refugee/displacement, natural disaster)?

What are the risk factors for different age groups of women and girls?

What are the risk factors for women and girls with different vulnerabilities (e.g. disabled, single women, married adolescents)?

What are protective factors for experiencing gender-based violence for women and girls living in humanitarian settings (conflict-affected, refugee/displacement, natural disaster)?

What are the protective factors for different age groups of women and girls?

What are the protective factors for women and girls with different vulnerabilities (e.g., disabled, single women, married adolescents)?

To conduct a systematic review of quantitative and qualitative studies of risk and protective factors for GBV in conflict and humanitarian settings, in order to inform the design on GBV prevention programs in these settings.

Study registration

The protocol has been registered with PROSEPERO (CRD42020198695).

Patient and public involvement

No patient involved.

Types of studies

This review will include research studies such as cross-sectional surveys, cohort and case-control studies, and qualitative studies. It will include data published in peer review articles as well as grey literature from non-governmental organizations (NGOs) or the United Nations. Existing systematic reviews will only be reviewed in order to identify potential original articles. Case studies will be excluded from the review.

Types of participants and settings

This review will focus on the experiences of women and girls who reside in conflict and humanitarian settings. This includes locations affected by natural disasters, armed conflict, refugee or displaced populations, including resettled refugees, and displaced persons who are part of the European migrant crisis. It will include respondents who are currently living in these settings and retrospective studies where participants recall events that previously occurred while they were resident in a conflict or humanitarian settings. For resettled refugees, we will only include articles that detail GBV experienced while still living in a humanitarian crisis.

The review is examining the risk and protective factors for GBV. This could include known risk and protective factors identified in non-conflict-affected or humanitarian settings, such as drug and alcohol use, poverty, education, childhood experiences of violence, and could include risk or protective factors unique to conflict or humanitarian settings.

Types of outcomes measures

The outcomes that will be the types of GBV experienced by women or girls. For this review, GBV will be defined as intimate partner violence (IPV—physical, sexual, psychological or economic), non-partner sexual violence, sexual abuse and exploitation, child, early and forced marriage, harmful practices (e.g., FGM), trafficking, abduction or femicide. These will be self-reported outcomes collected via household surveys, service-based data (e.g., health clinic records), or qualitative data.

Information sources and search strategy

A reference librarian specializing in systematic reviews was consulted to develop the search terms and target databases. The search will cover literature published between January 1995 and December 2020 to cover a period that aligns with a considerable increase in rigorous research efforts in humanitarian settings and an increase in international attention on preventing and responding to GBV.

The following databases will be searched: PubMed (Medline); PsycINFO; Scopus; Global Health; and Cochrane Center trials registrar. In addition, targeted searches of the following internet repositories will be conducted: What Works to Prevent Violence against Women and Girls Evidence Hub ( https://www.whatworks.co.za/ ); Prevention Collaborative Knowledge Platform ( https://prevention-collaborative.org/knowledge-platform/ ); GBV Prevention Network ( http://preventgbvafrica.org/understanding-vaw/vaw-resources/ ); UN Women’s Global Knowledge Platform to End Violence against Women ( https://evaw.unwomen.org/ ).

The general search strategy is attached in Additional file 1 and will be modified in line with the specific search functionality of each database. Grey literature databases will be manually searched by two reviewers to identify any potential articles that meet the search criteria.

Data collection and analysis

Eligibility criteria of the studies.

The inclusion criteria for the review will be the following:

Peer-reviewed articles or grey literature (e.g., Non-governmental agency (NGO) reports, United Nations reports) published in English

Studies published between January 1995 and December 2020

Studies documenting risk or protective factors for GBV (IPV, non-partner sexual violence, sexual abuse and exploitation, child, early and forced marriage, harmful practices (e.g., FGM), trafficking, and femicide, or abduction)

Studies of the experiences of women and girls in conflict or other humanitarian settings

Observational study or baseline analysis of a community-based evaluation

Articles that include either primary or secondary data analysis

The exclusion criteria for the review will be:

Studies published in languages other than English

Studies published outside the search dates or where the full text is not available through the searched database

Studies that do not examine risk or protective factors for GBV in conflict or humanitarian settings

Studies that do not utilize primary or secondary data analysis or are case studies.

Data management of the studies

The research team will utilize COVIDENCE ( www.covidence.org ) to manage the systematic review process. Results of the individual searches will be uploaded, and duplicates will be removed by the software. The inclusion and exclusion criteria for the review will be uploaded and two reviewers will be assigned roles through the COVIDENCE platform. All final citations of the included studies will be managed in RefWorks.

Data selection of the studies

Two reviewers will independently screen all titles and abstracts of the initially imported studies to assess their eligibility. Three categories will be utilized at this initial step: yes, no and maybe. Studies with two yes or two maybe votes will automatically advance to the full text screening. Studies with conflicting assessments (yes/no, yes/maybe, no/maybe) will initially be discussed by the two reviewers to determine if agreement can be reached. For studies where agreement is not possible, a third reviewer will be the final arbitrator. The process will be repeated with those studies that advance to the full text screening, with the possible assessments being reduced to yes or no. Two reviewers will initially independently vote and come together to discuss disagreements. A third reviewer will make the final assessment in the event consensus is not achieved.

Data extraction

Two reviewers will independently extract all relevant data items (e.g., risk and protective factors identified in the study, type of GBV) for the review. A third reviewer will randomly cross-check a selection of these to ensure no errors are made. Any disagreement between the two initial reviewers will also be resolved by this third reviewer. See Additional file 2 for a list of the data items that will be extracted. Data will be extracted and managed in Covidence.

The full list of data items to be extracted can be found in Additional file 2 . They include (1) general information and characteristics of the study, including the context of the study (e.g., armed conflict, natural disaster); (2) methodology, including the measures utilized, sample size and data analysis techniques; and (3) results, including identified protective and risk factors and the types of GBV explored.

Data synthesis and analysis

Extracted data will be analyzed utilizing narrative synthesis. For this, data will be grouped under a framework organized by the type of violence under investigation. To consider heterogeneity between quantitative versus qualitative results, two separate tables—one focusing only on qualitative studies and one focusing on quantitative—will be created. This will allow us to identify commonalities and differences in the results between the two types of data collection methods. For each table and then the resulting narrative synthesis common risk and protective factors for each type of violence will be categorized. During this process the evidence in support of each risk or protective factor will be assessed (e.g., study design, quality, strength of association if available). In general, the review will rely on the results reported in the published studies; however, the authors may request additional information or clarification from the corresponding authors of the studies if needed.

Appraisal/assessment of the quality of the included studies

After extraction, the study team will assess the quality of each individual study by examining the data collection methodology, sample size, data collection tools, and sampling methodology. Assessment of the quality of individual quantitative studies will be assessed using a criteria established by Rubenstein et al. for examining quantitative risk and protective factors for interpersonal violence [ 18 ]. This process includes a 7-point scale that includes (1) use of population-based sampling methods, (2) adequate sample size (500 participants), (3) adequate response rate (reported and 80%), (4) use of an established instrument for measuring violence, (5) clearly stated definitions for predictors, (6) study design accounts for temporality between predictors and violence, and (7) analysis captures different levels of violence or comparison process (e.g., linear regression, multinomial regression). This results in 0–7 point scale with 7 being highest quality.

For qualitative studies, quality will be assessed by adapting the criteria laid out in Mays and Pope [ 19 ]. To guide our quality assessment, a 5-point scale will be utilized considering (1) use of triangulation and perspectives of multiple stakeholder groups; (2) respondent validation; (3) clear exposition of methods of data collection and analysis; (4) reflexivity (sensitivity to the roles and biases of the data collectors and prior assumptions); and (5) attention to negative cases (details on contradictions or cases/opinions that deviate from the majority).

Finally, we will analyze the breath of the evidence included in the review using a modified GRADE system ( https://www.bmj.com/content/336/7650/924 ) using the below categories:

High quality —further research is very unlikely to change our confidence in the conclusion

Moderate quality —further research is likely to have an important impact on our confidence in the conclusions

Low quality —further research is very likely to have an important impact on our confidence in the conclusions

Very low quality —any conclusion is very uncertain

Two reviewers will independently review and make the initial assessment. A third reviewer will arbitrate any disagreement and make the final assessment if needed.

Presenting and reporting the results

Results of the review will be presented by type of GBV and then identified risk and protective factors. A PRIMA flowchart will be utilized to document the main steps and results of the review process itself. Data will be summarized narratively and with tables to summarize the key findings of each individual study. Quantitative and qualitative studies will be presented separately in tables and jointly considered in the narrative.

Ethical issues

As all data in review will be extracted from previously published studies, the study does not meet the requirements of human subject’s research and as such has been exempted from Institutional Review Board (IRB) review.

Publication plan

The review will be published in a peer-reviewed journal and will be utilized internally at the World Health Organization to inform efforts to prevent GBV in conflict and humanitarian settings.

GBV is a considerable challenge for women and girls in conflict and humanitarian settings. However, despite wide recognition of the scope of the problem, programs to prevent this violence and support survivors are often not evidence-based. The challenge of collecting rigorous data on GBV in these settings is immense, and there have been few academic studies exploring these issues due to limited funding, security issues, and other constraints. Given the difficulties in collecting primary data, the humanitarian community needs to better utilize the existing data that has been collected to gather lessons and inform programming. This review will add considerable knowledge to the evidence base as it will systematically identify, organize, and analyze the data that is available on risk and protective factors for GBV in these settings. This data is essential for programming seeking to design effective programs to reduce the risks of GBV in these settings and prevent new incidences of violence. In addition, the findings will help inform efforts to achieve the 2030 Sustainable Development Goals (SDGs) target 5.2 (“Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation”) by consolidating known data on drivers and risk factors for GBV in humanitarian settings [ 20 ]. It will provide an evidence-based framework for the development of new prevention programs that will help achieve this goal.

Availability of data and materials

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Abbreviations

• Gender-based violence

Institutional Review Board

Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols

WHO. Global and regional estimates of violence against women: prevalence and health effects of intimate partner violence and non-partner sexual violence. Geneva: World Health Organization; 2013. Available from: https://apps.who.int/iris/handle/10665/85239

Google Scholar  

Vu A, Adam A, Wirtz A, Pham K, Rubenstein L, Glass N, et al. The prevalence of sexual violence among female refugees in complex humanitarian emergencies: a systematic review and meta-analysis. PLoS Curr. 2014;6. https://doi.org/10.1371/currents.dis.835f10778fd80ae031aac12d3b533ca7 .

Stark L, Ager A. A systematic review of prevalence studies of gender-based violence in complex emergencies. Trauma Violence Abuse. 2011;12(3):127–34. https://doi.org/10.1177/1524838011404252 .

Article   PubMed   Google Scholar  

Global Women’s Institute, International Rescue Committee. No safe place: a lifetime of violence for conflict-affected women and girls in South Sudan. 2017. Available from: https://www2.gwu.edu/~mcs/gwi/No_Safe_Place_Full_Report.pdf

Kelly JTD, Colantuoni E, Robinson C, Decker MR. From the battlefield to the bedroom: a multilevel analysis of the links between political conflict and intimate partner violence in Liberia. BMJ Glob Health. 2018;3(2):e000668. https://doi.org/10.1136/bmjgh-2017-000668 .

Article   PubMed   PubMed Central   Google Scholar  

McAlpine A, Hossain M, Zimmerman C. Sex trafficking and sexual exploitation in settings affected by armed conflicts in Africa, Asia and the Middle East: systematic review. BMC Int Health Hum Rights. 2016;16(1):34. https://doi.org/10.1186/s12914-016-0107-x .

Murphy M, Bingenheimer JB, Ovince J, Ellsberg M, Contreras-Urbina M. The effects of conflict and displacement on violence against adolescent girls in South Sudan: the case of adolescent girls in the Protection of Civilian sites in Juba. Sex Reprod Health Matters. 2019;27(1):181–91. https://doi.org/10.1080/26410397.2019.1601965 .

Article   PubMed Central   Google Scholar  

Heise LL, Kotsadam A. Cross-national and multilevel correlates of partner violence: an analysis of data from population-based surveys. Lancet Glob Health. 2015;3(6):e332–40. https://doi.org/10.1016/S2214-109X(15)00013-3 .

Glass N, Perrin N, Marsh M, Clough A, Desgroppes A, Kaburu F, et al. Effectiveness of the communities care programme on change in social norms associated with gender-based violence (GBV) with residents in intervention compared with control districts in Mogadishu, Somalia. BMJ Open. 2019;9(3):e023819 Available from: https://bmjopen.bmj.com/content/9/3/e023819 .

Article   Google Scholar  

Bass JK, Annan J, McIvor Murray S, et al. Controlled trial of psychotherapy for Congolese survivors of sexual violence. N Engl J Med. 2013;368(23):2182–91 Available from: https://doi.org/10.1056/NEJMoa1211853 .

Article   CAS   Google Scholar  

Stark L, Asghar K, Seff I, Yu G, Tesfay Gessesse T, Ward L, et al. Preventing violence against refugee adolescent girls: findings from a cluster randomised controlled trial in Ethiopia. BMJ Glob Health. 2018;3(5):e000825. Available from: https://gh.bmj.com/content/3/5/e000825 . https://doi.org/10.1136/bmjgh-2018-000825 .

Asgary R, Emery E, Wong M. Systematic review of prevention and management strategies for the consequences of gender-based violence in refugee settings. Int Health. 2013;5(2):85–91. https://doi.org/10.1093/inthealth/iht009 .

Tappis H, Freeman J, Glass N, Doocy S. Effectiveness of interventions, programs and strategies for gender-based violence prevention in refugee populations: an integrative review. PLoS Curr. 2016;8. https://doi.org/10.1371/currents.dis.3a465b66f9327676d61eb8120eaa5499 .

Spangaro J, Adogu C, Ranmuthugala G, Powell Davies G, Steinacker L, Zwi A. What evidence exists for initiatives to reduce risk and incidence of sexual violence in armed conflict and other humanitarian crises? A systematic review. PLoS One. 2013;8(5):e62600. https://doi.org/10.1371/journal.pone.0062600 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Tol WA, Stavrou V, Greene MC, Mergenthaler C, van Ommeren M, García Moreno C. Sexual and gender-based violence in areas of armed conflict: a systematic review of mental health and psychosocial support interventions. Confl Health. 2013;7(1):16. https://doi.org/10.1186/1752-1505-7-16 .

Stavrou V. Sexual violence and armed conflict: a systematic review of psychosocial support interventions. In: García-Moreno C, Riecher-Rössler A, editors. Violence against women and mental health. Switzerland: Karger Publishers; 2013. p. 107–17. https://doi.org/10.1159/000342016 .

Chapter   Google Scholar  

Moher D, Liberati A, Tetzlaff J, Altman DG, PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J Clin Epidemiol. 2009;62(10):1006–12. https://doi.org/10.1016/j.jclinepi.2009.06.005 .

Rubenstein BL, Lu LZN, MacFarlane M, Stark L. Predictors of interpersonal violence in the household in humanitarian settings: a systematic review. Trauma Violence Abuse. 2020;21(1):31–44. https://doi.org/10.1177/1524838017738724 .

Mays N, Pope C. Qualitative research in health care. Assessing quality in qualitative research. BMJ. 2000;320(7226):50–2. https://doi.org/10.1136/bmj.320.7226.50 .

United Nations. Sustainable Development Goals. 2015. Department of Economic and Social Affairs. https://sdgs.un.org/goals .

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Acknowledgements

Thank you to Manuel Contreras-Urbina for his support in developing this protocol.

This work was supported by the World Health Organization (2020/997855-0). Representatives from WHO contributed to the development of the protocol (reviewing drafts and suggesting improvements).

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Maureen Murphy, Mary Ellsberg & Aminat Balogun

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MM led the protocol development. ME inputted into the design of the protocol. AB inputted into the design of the protocol. CGM inputted into the design of the protocol. All authors read and approved the final manuscript.

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Correspondence to Maureen Murphy .

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Supplementary Information

Additional file 1..

Search Strategy for the review.

Additional file 2.

Items for data extraction.

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Murphy, M., Ellsberg, M., Balogun, A. et al. Risk and protective factors for GBV among women and girls living in humanitarian setting: systematic review protocol. Syst Rev 10 , 238 (2021). https://doi.org/10.1186/s13643-021-01795-2

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A Systematic Review of Gender-Based Violence Prevention and Response Interventions for HIV Key Populations: Female Sex Workers, Men Who Have Sex With Men, and People Who Inject Drugs

Affiliations.

• 1 Department of Population, Family and Reproductive Health, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
• 2 Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
• 3 Department of Epidemiology, Center for Public Health and Human Rights, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
• 4 Continuum of Prevention, Care and Treatment of HIV/AIDS with Most at Risk Populations in Cameroon, CARE International, Yaoundé, Cameroon.
• 5 Continuum of Prevention, Care and Treatment of HIV/AIDS with Most at Risk Populations in Cameroon, CARE USA, New York City, NY, USA.
• PMID: 35144502
• DOI: 10.1177/15248380211029405

Gender-based violence (GBV) is that perpetrated based on sex, gender identity, or perceived adherence to socially defined gender norms. This human rights violation is disproportionately experienced by HIV key populations including female sex workers (FSW), people who inject drugs (PWID), and men who have sex with men (MSM). Consequently, addressing GBV is a global priority in HIV response. There is limited consensus about optimal interventions and little known about effectiveness. Our systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines and was registered in International Prospective Register of Systematic Reviews. Peer-reviewed and non-peer-reviewed literature were searched for articles that described a GBV prevention or response intervention specifically for key populations including FSW, PWID, and MSM. Results were organized by level(s) of implementation and pillars of a comprehensive GBV response: prevention, survivor support, and accountability/justice. Of 4,287 articles following removal of duplicates, 32 unique interventions (21 FSW, seven PWID, and nine MSM, not mutually exclusive) met inclusion criteria, representing 13 countries. Multisectoral interventions blended empowerment, advocacy, and crisis response with reductions in violence. Individual-level interventions included violence screening and response services. Violence-related safety promotion and risk reduction counseling within HIV risk reduction programming reduced violence. Quantitative evaluations were limited. Violence prevention and response interventions for FSW, PWID, and MSM span individual, community, and multisectoral levels with evidence of promising practices at each level. The strongest evidence supported addressing violence in the context of sexually transmitted infection/HIV risk reduction. As interventions continue to emerge, the rigor of accompanying evaluations must simultaneously advance to enable clarity on the health and safety impact of GBV prevention and response programming.

Keywords: HIV; gender-based violence; interventions; key populations; systematic review.

Publication types

• Research Support, N.I.H., Extramural
• Research Support, U.S. Gov't, Non-P.H.S.
• Systematic Review
• Drug Users*
• Gender Identity
• Gender-Based Violence* / prevention & control
• HIV Infections* / prevention & control
• Homosexuality, Male
• Sex Workers* / psychology
• Sexual and Gender Minorities*
• Substance Abuse, Intravenous* / complications

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• P30 AI094189/AI/NIAID NIH HHS/United States

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Is gender-based violence a confluence of culture? Empirical evidence from social media

Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India

Sourav Dandapat

Associated data.

The following information was supplied regarding data availability:

The data and code are available at Zenodo: Rimjhim, & Sourav Dandapat. (2022). Is Gender Based Violence a Confluence of Culture? Let’s look for empirical evidence via Social Media. https://doi.org/10.5281/zenodo.6820256 .

Gender-based violence (GBV) has been plaguing our society for long back. The severity of GBV has spurred research around understanding the causes and factors leading to GBV. Understanding factors and causes leading to GBV is helpful in planning and executing efficient policies to curb GBV. Past researches have claimed a country’s culture to be one of the driving reasons behind GBV. The culture of a country consists of cultural norms, societal rules, gender-based stereotypes, and social taboos which provoke GBV. These claims are supported by theoretical or small-scale survey-based research that suffers from under-representation and biases. With the advent of social media and, more importantly, location-tagged social media, huge ethnographic data are available, creating a platform for many sociological research. In this article, we also utilize huge social media data to verify the claim of confluence between GBV and the culture of a country. We first curate GBV content from different countries by collecting a large amount of data from Twitter. In order to explore the relationship between a country’s culture and GBV content, we performed correlation analyses between a country’s culture and its GBV content. The correlation results are further re-validated using graph-based methods. Through the findings of this research, we observed that countries with similar cultures also show similarity in GBV content, thus reconfirming the relationship between GBV and culture.

Introduction

Gender-based violence (GBV) is one of the most heinous and age-old violations of human rights ( https://www.who.int/news-room/fact-sheets/detail/violence-against-women ). GBV is evident across all parts of the globe ( https://www.undp.org/content/undp/en/home/blog/2018/violence-against-women-cause-consequence-inequality.html ), and it has been plaguing our society for a long back. The condition is so severe that one in three women is reported to have faced GBV ( https://www.who.int/news/item/09-03-2021-devastatingly-pervasive-1-in-3-women-globally-experience-violence ). With alarming instances of GBV around the world, social and governmental organisations are taking rigorous preventive measures. The quest to deliver effective preventive measures has triggered research to understand the causes and factors of GBV to provide effective preventive measures. Research in this field have found that cultural norms which comprise of societal stigma, gender-based rules, and societal prejudices are major factors that contribute to GBV ( Jewkes, Jama-Shai & Sikweyiya, 2017 ; Elischberger et al., 2018 ; Raj & Silverman, 2002 ; Jewkes, Levin & Penn-Kekana, 2002 ; Bishwajit, Sarker & Yaya, 2016 ). GBV is pervasive across all social, economic, and national strata ( Dartnall & Jewkes, 2013 ), but the type of GBV, the intensity of GBV, people’s reactions, and opinions for any GBV event is not the same across the globe. For example, acid attacks are a form of revenge in developing countries arising because of refusal of a marriage proposal or a love proposal, or land disputes ( Bahl & Syed, 2003 ). However, in South America, the same acid attack results from poor relationships and domestic intolerance toward women ( Guerrero, 2013 ). The context of GBV changes with the country, and this change is known to be an outcome of persisting culture in a country ( Abrahams et al., 2014 ; Fulu & Miedema, 2015 ; Alesina, Brioschi & La Ferrara, 2016 ; Perrin et al., 2019 ; Stubbs-Richardson, Rader & Cosby, 2018 ). The World Health Organization (WHO) has studied cultural norms of many countries leading to various forms of GBV ( https://apps.who.int/iris/bitstream/handle/10665/77936/9789241500845_eng.pdf?sequence=1isAllowed=y ). The global organization World Bank also pronounced to work on such cultural and social norms to curb GBV ( https://thedocs.worldbank.org/en/doc/656271571686555789-0090022019/original/ShiftingCulturalNormstoAddressGBV.pdf ).

However, these researches claiming cultural norms as a driving factor behind GBV are based on cognitive studies which require significant intervention from social and cultural experts. The claims presented in these works are based upon long-term manual discerning of GBV events occurring in countries of different cultures. These researches are dependent upon survey/questionnaire-based data which can be collected only in a limited amount and can also suffer from several biases. Thus, past research lacks a large-scale, data-driven empirical research to verify the confluences between culture and GBV.

In this article, we take a step to answer the research question ”Is gender-based violence a confluence of culture?” by experimenting with large-scale social network data. The use of social network data for research around GBV is a non-conventional way to dive into the finer details of GBV. Our research analyses GBV from the lens of culture. This research is useful for social workers, policy-makers, governments, and other organizations working for the welfare of women and society ( Kim, 2021 ). Additionally, the findings of this research can help in planning more efficient and targeted GBV policies and awareness campaigns. Social network data has already become a substitute for survey data for numerous applications. Recently, social network data has also gained much utility for research related to GBV ( Hansson, Sveningsson & Ganetz, 2021 ; Liu et al., 2019 ; Chowdhury et al., 2019 ; Hassan et al., 2020 ). Online content contains a rich spectrum of information pertaining to user opinions/reactions, ongoing news/events ( Blake et al., 2021 ), and many more ( Nikolov et al., 2015 ; Pal et al., 2018 ). Thus, online content is not only a mere content but a real-time proxy for user behaviour. For this research, we consider online content related to GBV from different countries as a representative of user reactions and perspectives towards GBV. We design experiments to check for the content similarity between countries with similar cultures. Towards this goal, we perform the correlation analysis between content distance and cultural distance between countries. Further, to validate results from the correlation analysis, we also performed graph analyses. In graph analyses, we create graphs with countries as nodes and different types of distances (content distance and cultural distance) between countries is used for building edges. These graphs are compared using various graph comparison metrics.

On experimentation with Twitter content from different countries, we find a statistically significant positive correlation between GBV content distance and cultural distance. We also observed a higher similarity between the GBV content graph and the culture graph. Thus, through the findings of this research, we observe that the countries which are similar in culture also show higher similarity in GBV content. This observation is consistent with correlation analyses and graph analyses. From this observation, we can conclude that there are traces of culture in GBV content which justifies the claim of confluences of culture on GBV. The contributions of the current research can be summarized as follows:

• • In this research, we explore evidence of confluence between GBV and the culture by means of an empirical study conducted over a large dataset created naturally over a long period of time on social media.
• • The results obtained from this research justify the hypothesis that GBV is a confluence of culture. This hypothesis has not been tested in past literature using uncensored and unbiased social media data.
• • All the experiments conducted in this research are extended to different categories of GBV and generic online content. Further, all the six dimensions of culture are also investigated. Thus, we provide a holistic analysis.
• • The findings in this research are supported by correlation analyses as well as graph-based analyses. Thus, making our claims more robust.

The rest of the article is organized as follows. ‘Related Works’ details relevant past literature related to this research. ‘Dataset and Processing’ describes the collected data. ‘Methodology’ elaborates on the methodology of our experiments, and ‘Results’ shows all the results and analyses. ‘Discussions’ discusses the implications and limitations of the work. Finally, ‘Conclusion and Future Work’ concludes our work with possible future works.

Related Works

This research is based upon three broad areas of related works i. The relation between GBV and culture ii. Social Media Content as a source of Data and iii. GBV through social media.

GBV and culture

GBV is a social ill evident across all the countries irrespective of their economy, language, and demography. However, with country, the type of GBV, its intensity, and the reaction of people vary ( Fakunmoju, Bammeke et al., 2017 ). For example, in the USA, dating violence is more common than in Africa where there are comparatively lesser instances of dating violence ( Johnson et al., 2015 ). On the other hand, in Africa, intimate partner violence is more prominent as compared to North America ( https://apps.who.int/iris/bitstream/handle/10665/85239/9789241564625_eng.pdf ). This implies that the same GBV is represented differently in a different country. This implies that GBV is a global evil but the context of GBV changes with the country. There have been many research to understand the causes and factors leading to GBV ( Jewkes, Levin & Penn-Kekana, 2002 ; Jewkes, Jama-Shai & Sikweyiya, 2017 ; Marine & Lewis, 2020 ). These works have claimed that a country’s culture can characterize the persisting GBV in the country. Every culture has norms, prejudices, and societal rules that design the behaviour of people towards GBV. For example, in Malawi, the concept of polygamy and dowry is evident in the culture, and these perpetuate GBV in Malawi ( Bisika, 2008 ). Similar research in many other countries like UK ( Aldridge, 2021 ), Ethiopia ( Le Mat et al., 2019 ), Cambodia  ( Palmer & Williams, 2017 ), and many other countries ( Djamba & Kimuna, 2015 ; Raj & Silverman, 2002 ) have highlighted cultural norms which lead to one or other form of GBV. Not only in research but global organisations like WHO ( https://www.who.int/violence_injury_prevention/violence/norms.pdf ), World Bank ( https://thedocs.worldbank.org/en/doc/656271571686555789-0090022019/original/ShiftingCulturalNormstoAddressGBV.pdf ) have also highlighted the cultural norms of many countries that influence GBV. The socio-cultural impact is so intense that people even justify instances of GBV as a form of the social norm which cannot be questioned ( Piedalue et al., 2020 ). However, these claims are supported by mere examples and small-scale interview-based data. Thus, the research community lags a data-driven research that justifies the claim with sufficient empirical results. In this research, we do a large-scale analysis of social network content to find evidence of confluence between the culture of a country and GBV. Next, we present the role of social media content in bridging the gap of data for various research.

Social media content

Social media has now become the new language of people, and this has generated a massive amount of data for various research. Social media data has removed the bottleneck of data requirements in numerous applications such as urban computing ( Silva et al., 2019 ), cultural computing ( Wang et al., 2017 ), personality computing ( Samani et al., 2018 ) and many more. Social media content also plays a huge role in understanding people’s views and sentiments during the COVID-19 pandemic ( Malagoli et al., 2021 ). Social media content has already substituted the tedious, time consuming, biased and under-represented survey-based data and has unlocked possibilities for research in many other directions. The utility of social media content increases with the availability of location-tagged data. The location-tagged online content has been used in numerous ethnographic research ( Abdullah et al., 2015 ), cultural research ( Cheke et al., 2020 ), and sociological research ( Stewart et al., 2017 ) in recent days. Social media content is not mere data, but it captures several dimensions of human interests, psychology, and behavior. There have been works which found that the social media content reflects the real-world properties as well.  García-Gavilanes, Mejova & Quercia (2014) ; Garcia-Gavilanes, Quercia & Jaimes (2013) have found that interaction and usage of social networks are dependent on social, economic, and cultural aspects of users. Thus, the real-world behavior of people is also mirrored in social networks. This utility of social media content motivates us to examine the relationship between GBV and a country’s culture through analysis of social media data. For this research, we use Twitter data related to GBV from different countries.

Measuring culture

Culture is an amalgamation of thoughts, beliefs, potential acts, and a lot more. A number of definitions of culture are available from previous works ( Hofstede, Hofstede & Minkov, 2005 ). One of many definitions of culture is “a fuzzy set of assumptions and values, orientations to life, beliefs, policies, procedures and behavioural conventions that are shared by a group of people”  ( Spencer-Oatey & Franklin, 2012 ). Culture plays a vital role in many spheres of life, such as behaviour ( Huang, Beshai & Yu, 2016 ) economy ( Herrero, Jiménez & Alcalde, 2021 ), language ( Sazzed, 2021 ), attitude ( Shin, Chotiyaputta & Zaid, 2022 ). In order to ease out research based on culture, there have been several quantification of culture. Hofstede has done one such comprehensive quantification. Hofstede ( Hofstede, Hofstede & Minkov, 2005 ) defines culture in terms of six parameters ( Power Distance, Uncertainty Avoidance, Individualism, Masculinity, Long Term Orientation, Indulgence ) and quantifies each one for different countries of the world. An extensive set of previous works use Hofstede Dimensions to quantify culture ( García-Gavilanes, Mejova & Quercia, 2014 ; Garcia-Gavilanes, Quercia & Jaimes, 2013 ; Prakash & Majumdar, 2021 ). For this research, we also used Hofstede’s dimensions which have been used in a huge number of research to measure culture.

GBV through the lens of social media

Social media provides an uncensored and user-friendly medium for expressing views and opinions ( Puente, Maceiras & Romero, 2021 ). With this, social media has become a platform for self-expression as well as for conducting online campaigns ( Martínez, Pacheco & Galicia, 2021 ). There have been many campaigns on social media related to GBV like the #metoo, #Notokay, #StateOfWomen, #HeForShe and many more ( Karuna et al., 2016 ). These campaigns and freedom of expression on social media have generated huge data related to GBV. The recent campaign of #Metoo observed an unprecedented response from all around the globe, thus, generating huge data related to GBV. And the event was followed by a sudden upsurge in research related to GBV using the generated data.

Thus, the data availability of social media has helped in many recent works related to GBV, which have delivered a multitude of interesting findings ( Moitra, Ahmed & Chandra, 2021 ; Razi, 2020 ; Pandey et al., 2018 ; Khatua, Cambria & Khatua, 2018 ). Moreover, location-tagged social media data also assist in several cross-cultural studies related to GBV ( Purohit et al., 2015 ; Starkey et al., 2019 ). In this article, we also used social media Twitter data from different countries of the world as a source of data.

Methodology

In this section, we first give details of the collected dataset and its processing. Further, in the section, we elaborate on the methodology used to understand the relation between GBV and culture. We process the country-level tweets from different categories for correlation analysis and graph analysis w.r.t culture of different countries. The flow of methodology is represented in Fig. 1 . Next, we explain the details of data collection and its processing to obtain country-wise GBV tweets.

Dataset and processing

We use public streams of Twitter data collected using the Twitter Streaming API. We procured 1% of public tweets provided by the API for a period of two years and five months (1st July 2016–25th Nov 2018). We remove all the duplicate tweets and retweets from the collected data as these do not add any new information ( Cheke et al., 2020 ). From the collected tweets, we extracted GBV related tweets using a keyword matching approach as described next.

GBV Tweet extraction

UNFPA (United Nations Population Fund) domain experts have proposed three categories of GBV, namely sexual violence , physical violence , and harmful practices . They have also provided unique keywords related to each category of GBV, which have been used frequently in past literature for GBV related research ( Purohit et al., 2015 ; ElSherief, Belding & Nguyen, 2017 ). Table 1 shows a total of 81 keywords constituting 29, 25, and 27 keywords from sexual violence, physical violence, and harmful practices respectively. We use the same keywords to extract relevant tweets from all three categories. The keyword set provided by UNFPA is very precise and can contain multi-words. Our methodology for extracting tweets for a particular keyword is based on the presence of the keyword in a tweet. If all the words of a multi-word keyword are present in a tweet regardless of order, we consider it a match. For example, for the category sexual violence, sexual assault is a related keyword with two words. If a tweet contains both the words sexual and assault , we consider it a match. For the cases where a tweet matches more than one category, we consider the tweet in both categories of GBV. This approach has been used in previous works in order to deliver high-precision data ( Purohit et al., 2015 ). From the keywords related to each category of GBV, we extract tweets and create a tweet dataset from three categories, namely the sexual violence dataset, physical violence dataset, and harmful practices dataset, with a total of 0.83 million , 0.53 million , and 0.66 million tweets, respectively. Further, we combined all three category tweets to create a GBV tweet dataset containing more than two million tweets.

Generic Tweet dataset

We created another dataset, the generic tweet dataset , to provide a better context of comparison with other categories of the dataset. This dataset is used for drawing inferences from GBV categories dataset w.r.t a generic dataset. For creating this dataset, we borrowed the methodology of ElSherief, Belding & Nguyen (2017) . Our collected data is for a very long period, resulting in around 4 billion tweets. We extracted a random 1% sub-sample of total collected tweets as a generic tweet dataset . To eliminate duplicate content here as well, we removed tweets which are duplicates and retweets. Details of generic tweets data are given in Table 2 .

Country-level location tagging

There are many indicators of location in a tweet, such as geotags , time zone , and profile location . Adopting the location indicators of Annamoradnejad et al. (2019) for tagging each tweet to a location, we use a three-level hierarchy of location indicative according to their accuracy levels ( Kulshrestha et al., 2012 ). The first one is geotag, which gives the most accurate location information. If a geotag is available, then we use it for location tagging, and if it is not present, we look for the time zone data. Time zone is also an accurate way to tag country-level locations. A time zone data directly contains the user’s time zone in the form of the corresponding country name. For the cases where even time zone information is not available, then we look for the next location information in the hierarchy, i.e., location field mentioned in the user profile. Geotags and time zone contain exact country names, which can be directly mapped to a country. User profile location is an unstructured text location field that requires further processing to get country information. For this, we use the approach used by Dredze, Osborne & Kambadur (2016) where city names present in the user profile location are mapped to corresponding country names based on the Geoname ( https://pypi.org/project/geonames/ ) world gazetteer. We borrow the list of required countries from Annamoradnejad et al. (2019) where authors have used a list of 22 countries, namely Arab countries, Argentina, Australia, Brazil, Canada, China, Colombia, France, Germany, India, Indonesia, Iran, Italy, Japan, Korea, Philippines, Russia, Spain, Thailand, Turkey, UK (United Kingdom), USA(United States of America) . Arab Countries is a group of countries with a similar culture, so we merged tweets from all Arab Countries . There were very few tweets from Korea (170), so we discarded Korea from the list of considered countries and limited our research to the remaining 21 countries, each having more than 3,000 tweets. We apply the same location tagging scheme to all the GBV tweets and generic tweets. The complete data statistics are shown in Table 2 for all the categories of tweets.

We present the evaluation methodology and evaluation results of GBV tweets extraction and location tagging in ‘Results’. In this research, we want to explore the relationship between GBV online content and culture of a country. For this, we perform two analyses i. Correlation Analyses and ii. Graph Analyses. In correlation analysis, we correlate culture and its dimensions with different categories of online content in order to understand their relationship. In graph analyses, we create country graphs on the basis of parameters correlated in correlation analyses like content, and culture, which are compared using multiple graph comparison metrics in order to re-assure the observed relationships from correlation analyses. Next, we discuss the methodology used for these analyses.

Correlation analysis

In order to find a relation between the culture of a country and GBV, we calculate cultural distance and content distance between each pair of countries as detailed next.

Cultural distance

We quantify the cultural distance between two countries using cultural dimensions proposed by Geert Hofstede ( Hofstede, Hofstede & Minkov, 2005 ). Geert Hofstede administered a huge survey among people from different countries to measure the difference in the way they behave. He has quantified six dimensions of culture ( power distance 1 , individualism 2 , masculinity 3 , uncertainty avoidance 4 , long-term orientation 5 , indulgence 6 ) for different countries in values ranging between 0–120. In order to measure cultural distance between two countries, we adopt the formulation of ( Annamoradnejad et al., 2019 ) where authors use the euclidean distance between two countries to measure the cultural distance. The cultural distance can be formulated as shown in Eq. (1) , where | D | is the total number of dimensions, d c 1 i and d c 2 i are the values of dimension d i for countries c 1 , c 2 respectively.

We also calculate the distance between countries on the basis of each dimension of culture proposed by Hofstede. For example, power distance is one of the dimensions of culture, and we need to calculate the distance between two countries according to power distance . For this also, we use euclidean distance, but since there is only one parameter, this becomes equivalent to | d c 1  −  d c 2 |. For further analyses, we calculate the cultural distance for each pair of countries on the basis of culture and six dimensions of culture.

Content distance

Online content related to a particular topic from a particular country captures country-level user comments and discussions on that topic ( Cheke et al., 2020 ). In order to measure the difference between contents from two countries, we measure the content distance between two countries using Jaccard Similarity. First, all the tweets from each country are pre-processed to generate country-wise tweet tokens, details of which are given next.

Tweet preprocessing

We adopt the pre-processing settings of Cheke et al. (2020) to generate tweet tokens from each country. We first remove URLs, mentions, punctuation, extra spaces, stop words, and emoticons. Online acronyms and short forms are expanded using NetLingo ( https://www.netlingo.com/acronyms.php ). For hashtags, we removed the symbol # and kept the remaining word. Spelling and typos are corrected using Textblob ( https://textblob.readthedocs.io/en/dev/ ). We also transliterated non-English words to English to reduce inconsistencies in language. Lastly, we tokenized each tweet using NLTK (Natural Language Toolkit). Extracted tokens from all the tweets of a country are merged to create country-wise tweet tokens. Next, for each pair of countries, we calculate content distance using the formula shown in Eq. (2) , where C 1 , C 2 are the set of all the tweet tokens of countries C 1 and C 2 , respectively, and | C 1 ∩ C 2 | | C 1 ∪ C 2 | is the Jaccard Similarity. 7

We have a total of 5 categories of online content i.e., sexual violence, physical violence, harmful practices, GBV, and generic content. We apply the same methodology to extract country-wise tweet tokens from each category of online content.

Correlation

A correlation helps in understanding the relationship between two variables. Pearson correlation and Spearman correlation are two popular metrics for correlation. To establish robustness in our findings, we use both Pearson correlation , and Spearman correlation for calculating the association between content distance and cultural distance . Pearson correlation captures the linear relationship between two variables and Spearman correlation captures the monotonic relationship between two variables. Both the correlation metrics give correlation values in the range of (−1 to +1). A positive correlation value indicates that content similarity is higher for countries having higher cultural similarity and a negative correlation indicates vice versa. For calculating the correlation, we calculated content distance and corresponding cultural distance for each country pair (a total of n C 2 pairs, if there are n countries). For exhaustive correlation analysis, we measured multiple correlations by keeping one correlation variable as different types of content (sexual, physical, harmful, GBV, and generic tweets) and another variable as six dimensions of culture.

For measuring the fitness of a correlation, we calculated the p -values for each correlation using the python library SciPy ( https://docs.scipy.org/doc/scipy-0.14.0/reference/stats.html ). A p -value represents the measure of occurrence of the correlation between two data samples by chance.

Graph analysis

Our objective is to compare GBV related content to a country’s culture. To this end, we created country graphs where edge weights are decided on the basis of different distances in terms of GBV content and culture, as mentioned in ‘Correlation Analysis’. For detailed analyses, we created multiple weighted graphs among countries with a different edge parameters. Finally, we compared created graphs using multiple graph distance metrics and graph clustering.

Country graph : A country graph created in this research is an un-directed, weighted graph G  = ( C ,  E ), where C denotes the nodes of the graph, which are countries, and E denotes the set of edges between countries. For all the graphs in this research, the set of nodes C and the set of edges E are the same. The only difference is in the weights of the edges. Next, we describe the creation of edges in the required graphs.

GBV content graph : This graph captures the relationship between countries according to GBV content distance. In GBV content graph G gbv  = ( C ,  E ), the weights of the set of edges E are decided on the basis of the content distance score between two countries on the basis of GBV tweets. Here, GBV tweets are used for calculating content distance. We also create sexual violence graph , physical violence graph , and harmful practices graph where for assigning edge weights, we calculate content distance on sexual violence tweets, physical violence tweets, and harmful practices tweets, respectively.

Generic content Graph : This graph captures the relationship between countries and generic content. In the generic content graph G rand  = ( C ,  E ), the weights of the edges are assigned according to the content distance score between two countries on the basis of generic tweet data.

Cultural graph : This graph captures the cultural relationship between countries. In the cultural graph G cult  = ( C ,  E ), the weights of the edges are decided by the value of cultural distance calculated using Eq. (1) .

Graph Pre-processing : For all the graphs G  = ( C ,  E ), there is an edge between any pair of countries with a weight creating a complete graph. Further, all the created graphs have a different range of values of edge weight. For example, for GBV tweets graphs, edge weights will lie in the range (0,1), but for the culture graph, the values of weights can range from (0–120). To ensure consistency, we upscale edge weights in the range of (0,1) to a range of (0–120). Next, we pruned edges that are unimportant, i.e., whose weight is lesser than the median of all the edge weights. Thus, keeping only the important, i.e., higher edge weight edges in the graph. Before pre-processing, each graph is a complete graph with the same edges in all the graphs, but after pre-processing, each graph is a non-complete graph with only important edges resulting in different edges in each graph. The same is applied to all the graphs, and the final pre-processed graph is a weighted, un-directed, and non-complete graph.

We also mention that all the distances (content/culture) used to decide edge weight in all the graphs follow the axioms of distance ( Kosub, 2019 ).

Graph comparison metrics

For comparing two graphs, past literature has proposed a number of metrics depending upon the type of graphs ( Tantardini et al., 2019 ; McCabe et al., 2020 ). In this research, we have graphs with node correspondence, i.e., same nodes in every graph. Additionally, our graphs are un-directed and weighted. For the purpose of graph comparison, we use multiple graph distance metrics to calculate the distance between two graphs. Graph distance shows how dissimilar the two graphs are. For calculating graph distance, we used the Python library netrd ( https://netrd.readthedocs.io/en/latest/distance.html ). Next, we describe metrics used in our research to calculate graph distance.

• • Quantum JSD: Quantum Jensen–Shannon Divergence ( De Domenico & Biamonte, 2016 ) compares two weighted and undirected graphs by finding the distance between spectral entropy of density matrices.
• • Degree divergence: This method ( Hébert-Dufresne, Grochow & Allard, 2016 ) compares the degree distribution of two graphs. This methodology is applicable to weighted as well as unweighted graphs but only undirected graphs.
• • Jaccard distance: Jaccard distance ( Oggier & Datta, 2021 ) is applicable to only unweighted graphs, and its value depends on the number of common edges in the two compared graphs. For applying to our graphs, we coerced weighted graphs into unweighted ones by removing weights from all the graphs.
• • Hamming distance: Hamming distance is one of the popular techniques for measuring the distance between two unweighted graphs. This is a measure of element-wise disagreement between the two adjacency matrices of the graphs. We applied Hamming distance to our graphs by coercing weighted graphs to unweighted ones by simply removing the weights.
• • HammingIpsenMikhailov: This method is an enhanced version of Hamming Distance which takes into account the disagreement between adjacency matrices and associated laplacian matrices. This is applicable to weighted and undirected graphs.
• • Frobenius: This is an adjacency matrix level distance that calculates L2-norms of the adjacency matrices.
• • NetLSD: A metric for measuring graph distance based on spectral node signature distributions for unweighted graphs. For this, we coerced our graphs to unweighted ones by removing the weights.

Graph clustering

A graph clustering algorithm clusters similar nodes in different groups. If two graphs are similar, then their clusters will also be similar. In order to compare the GBV graph and the generic graph with the culture graph, we used the Louvain community detection algorithm ( De Meo et al., 2011 ). Louvain community detection algorithm is a clustering algorithm for nodes of a weighted graph where nodes are clustered on the basis of modularity between the nodes. Here the number of clusters was decided by the algorithm only.

In this section, we first provide validation results for our proposed methodology of GBV tweet filtering and location tagging. Then we present the results and insights of correlation analyses and graph analyses in order to understand the correspondence between GBV and culture.

GBV tweet extraction and error analysis

GBV tweet extraction is accomplished by tagging tweets using a keyword matching process. Following the keyword match verification methodology of Cheke et al. (2020) , we employed three graduate annotators to manually tag the GBV category. Annotators were provided with a sample of tweets without any category information and were asked to manually tag each tweet to one or more categories of GBV (sexual violence, physical violence, harmful practices) with their own understanding and external online resources. Annotators were provided with a basic definition of GBV and its categories. For the purpose of validation, we created a balanced and shuffled sample of 6,000 tweets with 2,000 tweets from each category of GBV. For each tweet annotated by the three annotators, we select the majority category as the final category. Tweets that do not have any majority category are discarded. Considering the category tagged by annotators as the actual categories, we calculate the precision value of our keyword matching methodology for each category of GBV. The precision value for sexual violence is found to be 0.97, for physical violence 0.96, and for harmful practices to be 0.98.

Table 3 shows a few example tweets and tagged GBV categories. Examples 1–9 shows matching keywords and the tagged GBV category of the tweets from all three categories of GBV. Example 10–11 show tweets that contain keywords from more than a category of GBV. These tweets are kept in all the matching categories. In examples 12–13, keyword matching results in the wrong tagging of tweets because of contextual differences in tweets. As we can see in example tweet 12, the keywords woman and attacked belong to physical violence keywords, and hence the tweet is wrongly classified in the physical violence category. There are only a few such errors in GBV tweet category tagging arising because of changes in the context of tweets.

Evaluation of location tagging

We have a three-level hierarchy(time zone, geotags, profile location) of location tagging. Location tagging from time zone and geotags is completely accurate. For evaluating location tagging from profile location a random sample of 10, 000 tweets are given to three independent graduate annotators who were asked to manually tag a country-level location from their own understanding using online gazetteers and searches. The majority country name is selected as the final tagged country name. The profile location field with no majority among annotators is discarded. Considering the country tagged by manual annotation as the actual country, we obtained a precision score of 0.94.

Tables 4 and ​ and5 5 show the results of pearson correlation and spearman correlation of different types of online content with culture and its parameters. From the tables, we can draw the following observations.

All the correlations all calculated on a sample of 210 country pairs, since there are 21 countries. Degree of freedom for the correlation analyses is 208.

• 1. GBV content and all categories of GBV content show a positive correlation with culture and all its parameters by both the correlation metrics. For example, the correlation between GBV content and culture is 0.55, with a significant p -value of 0.001. Similarly, the correlation between culture and other categories of GBV i.e., sexual violence, physical violence, and harmful practices content is 0.51, 0.53, and 0.53, respectively with significant p -values.
• 2. The three parameters of culture uncertainty avoidance , indulgence , and individualism show comparatively higher correlation values as compared to other parameters of culture power distance , masculinity , and long-term orientation . This observation is consistent with all the categories of GBV content and with both the correlation metrics. The Pearson correlation of uncertainty avoidance , indulgence , and individualism with GBV tweet content is 0.36, 0.34, and 0.45 ( Table 1 ). On the other hand, the pearson correlation of power distance , masculinity , and long-term orientation with GBV tweets content is 0.27, 0.16, and 0.17 respectively.
• 3. We also observe that the same content analyses performed for GBV content did not show a similar strong and consistent correlation with generic content. The Pearson correlation between generic content and culture is 0.33, which is much lower than the Pearson correlation between GBV content and culture, i.e., 0.55. Additionally, generic content fails to show any correlation with culture and its parameters from spearman correlation.

Observation 1 indicates that GBV content has an influence of culture and all six parameters of culture. The observation is consistent with all the categories of GBV content, i.e., sexual violence, physical violence, and harmful practices. Additionally, we also show the scatter plots of the culture and different GBV content types in Fig. 2 to reconfirm the findings. All these results imply that countries with similar culture also show higher similarity in GBV content. GBV content is composed of discussions, news, comments generated by users on the topic related to GBV. The reason for the similarity in GBV content for similar culture countries is the similarity in their discussions, news, and comments. For further understanding, we manually discerned the content of similar culture countries. According to Hofstede’s dimensions, the USA and Canada are more similar 8 in culture as compared to USA and Iran. Similarly, Iran is more similar to Arab countries as compared to the USA. Table 6 shows the scores of cultural distance between different countries using Hofstede’s dimensions. In order to show the content differences of different culture countries, we ploted the word clouds of common frequent words of USA-Canada and Arab countries-Iran in Figs. 3A and ​ and3B, 3B , respectively. For the countries USA and Canada, we found keywords like gfriend, whitesupremacist, objectifying as common frequent keywords. For the countries Arab countries and Iran, we found keywords like veiled, hijab, attacked, predator as common frequent keywords.

The highlights in observation 2 suggest that a few parameters of culture also play an important role in shaping the content related to GBV. Interestingly, Hofstede’s parameters uncertainty avoidance, indulgence, and individualism are found to show more impact on GBV related content than other parameters like power distance, masculinity, and long term orientation. For further reconfirming the connection between these parameters and different types of GBV content, we also show the scatter plots of these parameters and different content in Figs. 4 , ​ ,5 5 and ​ and6. 6 . From the scatter plots, we can evidently observe that Hofstede’s parameters uncertainty avoidance, indulgence, and individualism consistently show a close association (points are closer to the fitted line) with all types of GBV content. The same is not true for generic content. This shows a connection between these parameters of culture and different GBV categories content. The reasons for more influence of these parameters require further exploration which is outside the scope of this work. However, this observation again recommends a role of culture on GBV.

Observation 3 further strengthens the findings of Observations 1 and 2. The pattern of correlation showing a connection between culture and different categories of GBV is not the same for generic content. The lower and inconsistent correlation values of the generic content as compared to GBV content reinforce a stronger relationship between GBV content and the culture of a country. Further, the scatter plots shown in Figs. 2 , ​ ,4, 4 , ​ ,5 5 and ​ and6 6 also show that the points in all the plots of generic content are more scattered from the fitted line as compared to points in GBV and its category content plots. For example, the points in the GBV-culture plot ( Fig. 2A ) are closer to the fitted line, while in the generic-culture plot ( Fig. 2E ), the points are farther to the fitted lines showing a comparatively lower correlation. Other categories of content (sexual, physical, and harmful) also show a stronger correlation with culture as compared to generic content.

Generic content is composed of content from different topics, a few of which can be highly correlated to culture ( Cheke et al., 2020 ), such as food, and a few can hardly show any correlation ( Annamoradnejad et al., 2019 ), such as technology. These characteristics of generic content can be the most probable reason for showing weak correlations. Here we showed results of generic content just to provide a broader background for understanding. Next, we describe the results from graph analyses in order to validate findings from correlation analyses.

We first summarize the statistical characteristics of the created graphs in our research in Table 7 . All the graphs show similar basic properties because there is node correspondence in all the graphs. The edges and edge weights are the main varying parameter in the graphs. Next, we explain the details of graph comparison.

Table 8 shows the distance between different created graphs from various metrics. For each distance metric, if the distance value between a pair of graphs (G1, G2) is smaller than the distance between another pair of graphs (G3, G2), it means that the graph G2 is more similar to G1 as compared to G3. From the table, we observe that for all the metrics, the distance between the generic tweet graph and the culture graph is consistently higher than the distance between other graphs (GBV-culture, sexual violence-culture, physical violence-culture, and harmful practices-culture). For example, the metric QuantumJSD , the distance between generic graph and culture graph is 0.27 while for GBV graph and culture graph is 0.21. For the same metric, the distance between the sexual graph-culture graph, the physical graph-culture graph, and the harmful graph-culture graph is 0.21, 0.20, and 0.22, respectively. This shows that the graph created using content distance by GBV and its categories are more similar among themselves and to the graph created using cultural distance. The graph created using the generic content is consistently more distant from the cultural graph as compared to other graphs. This observation re-validates the observation from correlation analyses showing a higher degree of similarity between GBV content for similar culture countries. As two similar graphs show lesser distance by graph distance metrics, two similar graphs will also show similar clusters. Next, we present clustering in the created graphs.

Clustering the graphs

Figure 7 shows the plots of clusters in the culture graph, GBV graph, and generic content graph. From the created clusters, we observe that the clusters in the culture graph are more similar to GBV graph as compared to generic graph. The countries in the culture graph which belong to the same cluster have a larger overlap with the GBV graph rather than the generic graph. For example, the countries USA, UK, and Australia belong to the same cluster in the culture graph, and the same is also true for GBV graph. However, for the generic graph, all three countries belong to different clusters. This observation again shows a higher similarity between the culture graph and the GBV graph than the generic graph and the culture graph. Thus, we observe that the created clusters are also congruous to all other findings stating a higher level of relation between culture and GBV content, which is not the same for generic content.

Discussions

Implications.

In this research, we use social media data to verify connections between the culture of a country and GBV. Our findings suggest that real-world hypotheses are also evident in social media data, and their verification is no longer dependent on survey-based data. We believe that this research not only validates the hypothesis of confluence between culture and GBV but also points to the possibility of verification of other hypotheses related to GBV.

A finer analysis can also reveal culture-specific traits of GBV, which can further enhance understanding of GBV across cultures. We argue that these analyses are vital for designing culture-aware policies and strategies to curb GBV. There is a huge possibility of discovery of many more cultural norms like those pronounced by the World Bank ( https://thedocs.worldbank.org/en/doc/656271571686555789-0090022019/original/ShiftingCulturalNormstoAddressGBV.pdf ), which can promote GBV. Thus, this research paves a path for understanding culture-specific GBV using online social network data.

Limitations and critiques

In this section, we show a few possible limitations and how our research overcomes those. In this article, we have performed cross-cultural research using online content from Twitter. Here, we limit our research to English tweets only pertaining to two reasons. First, the GBV keywords are in English, resulting in a collection of English GBV content. Second, English has become the new lingua franca on Twitter ( Choudhary et al., 2018 ), which delivers sufficient tweets for this research.

GBV data collection is based upon GBV keywords provided by UNFPA, which is a global organization. The provided keywords can be incomplete and non-exhaustive. There might have scope for increasing these keywords; however, GBV is a sensitive issue, and extending keywords without the intervention of social experts may introduce errors. So, we limit this research to globally available keywords only.

Online content is much inflected by a flux of ongoing news and events, which can lead to differences in data patterns in certain time periods. However, our research is based upon data from a long temporal span which diffuses such temporal inflections ( Grieve, Nini & Guo, 2018 ).

There can be many more ways to capture the distance between countries in terms of GBV, but we have limited this to content distance using two common metrics (cosine similarity and jaccard similarity). The content distance used in this research captures the basic difference between tweet tokens of the two countries. However, the same methodology can be easily adapted to other twitter features and metrics.

Conclusion and Future Work

The article investigates evidence of the confluence between culture and GBV with the help of social media content. Social media content is explored by means of correlation analyses and graph-based analyses to find the traces of culture in GBV related social media content. In this research, we find a noteworthy influence of culture on GBV related content which is not apparent in generic content. The observation is consistent with different analyses and metrics. This research not only claims higher confluence between GBV and culture but also paves a path for effective policy-making and research related to GBV by means of social media content. Social media content captures behavioral aspects related to GBV, which can be used for other investigations related to GBV. As a future work of this research, we would like to understand the role of other factors like economy, unemployment,and crises in GBV. Moreover, this research is a global analysis of different countries of the world. We would also like to extend the research to a finer scale of states or counties within a country.

Funding Statement

The authors received no funding for this work.

Additional Information and Declarations

The authors declare there are no competing interests.

Rimjhim conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

Sourav Dandapat conceived and designed the experiments, performed the experiments, analyzed the data, performed the computation work, prepared figures and/or tables, authored or reviewed drafts of the article, and approved the final draft.

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Literature review on school-related gender-based violence: how it is defined and studied

A review of the SRGBV literature serves several purposes. First, it identifies overarching SRGBV types or categories with the intent to assist researchers and the international development community to align more closely around common SRGBV definitions. Greater definitional agreement will contribute to the expansion of the evidence on effective SRGBV interventions and will allow for greater comparability of research, and the identification of research gaps. Second, this review provides a global overview of the common methodologies observed across SRGBV studies and evaluations and contributes to a clearer understanding of the research trends, strengths, and weaknesses for consideration when conducting studies and evaluations of SRGBV. The findings serve to better inform SRGBV prevention activities, future investigations of SRGBV and more effective measurement of SRGBV. Third, this review is informing USAID's development of a companion document, Conceptual Framework for Measuring School-Related Gender-Based Violence, that will provide development partners and researchers with a conceptual framework and measurement tools to inform programming and research protocols. Finally, a review of the methodologies used to examine SRGBV may provide guidance to policymakers, other program designers and researchers in many countries who are grappling with the same set of issues around SRGBV. The wide variety of sectors where SRGBV is studied underscores the comprehensive nature of the SRGBV issue and the broad base of factors that mediate its occurrence, manner of presentation, and intensity. Tapping into this broad literature base is important because it will extend the frame from which researchers and implementers design further investigations and prevention programs.

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Sexual and gender based violence in africa: literature review.

Population Council

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Publication date.

This report provides details of a literature review that is intended to inform partners in the Population Council–coordinated regional network that aims to develop a multisectoral and comprehensive response to sexual and gender-based violence (SGBV) in Eastern, Central, and Southern Africa. The first section defines the terminology used throughout the review, and discusses the prevalence and consequences of SGBV, considers the implications of the disproportionate programmatic focus on adult women survivors, and explores approaches to managing child and male survivors of sexual violence. The second section presents regional policies and programming relating to the medical and psychosocial management of survivors. The third section discusses the forensic, referral, and judicial requirements of successful prosecutions. And the fourth section considers the role that community and institutional linkages play in the prevention of SGBV, and examines the extent to which violence is addressed through messages communicated during prevention strategies, and through routine screening.

Recommended Citation

"Sexual and gender based violence in Africa: Literature review." Nairobi: Population Council, 2008.

10.31899/rh5.1019

Expanding the Evidence Base on Comprehensive Care for Survivors of Sexual Violence in East and Southern Africa

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'Blue wall of silence' protects police officers accused of gender-based violence, victims say

Cbc news data reveals 1 in 3 suspensions across ontario involved allegations of domestic abuse, sexual assault.

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WARNING: This article contains graphic content and may affect those who have experienced​ ​​​sexual violence or know someone affected by it.

She was a sergeant and he was a constable. They met at work and became a power couple, sporting matching police badges. 

But their love story would end in violence. It didn't matter that she outranked him — what happened flipped their power balance.

"It was just a slow burn downhill into what became a serious situation of coercive control, which I didn't see that clearly at the time," she said. 

It began with verbal insults, but the woman said her ex-husband became increasingly controlling. He even used a listening device to spy on her, according to court files.

It all came to a head one night in July 2019.

"There was an explosive confrontation," she said. "[He was] chasing me and the kids around the house and we barricaded ourselves in the bedroom and he broke in and my son was trying to defend me. It was such a scene." 

She called her boss, then escaped the house.

"I went to my own police headquarters in my pyjama shirt," she said. 

The police were concerned her husband might be armed, so they dispatched a tactical unit. 

"My kids saw their dad get arrested," said the sergeant, who CBC News has agreed not to name to protect her safety. "I had to stay in the domestic violence office until they could execute a search warrant because they couldn't find his gun. It wasn't where it was supposed to be at work."

Her then husband was locked up in jail while officers searched the couple's home. Police eventually charged him with intercepting private communications, assault, extortion and careless storage of a firearm.

In the literature on gender-based violence, police are disproportionately perpetrators of particularly domestic violence.​​ - Danielle McNabb, Brock University

The officer from the Greater Toronto Area (GTA) was suspended with pay. He would go on to stay home for 646 days, collecting almost $300,000 in salary before he finally resigned in 2021. 

Throughout the ordeal, his ex-wife said she was the one treated as the outcast.

"No one came to me and said, 'OK, we've rallied the troops. This is what to expect. This is how you go back to work,'" she recalled. "None of that happened."

Her ex is among the more than 453 cases of suspensions with pay in Ontario over the past decade at a cost of $134 million, according to an exclusive CBC investigation. 

CBC's research found that more than one-third of the allegations leading to officer suspensions since 2013 involved gender-based violence including sexual assault, domestic violence or sexual harassment.

"In the literature on gender-based violence, police are disproportionately perpetrators of particularly domestic violence," said Danielle McNabb, an assistant professor who researches Canadian public law at Brock University.

"I do think that this really constitutes the tip of the iceberg from what we know about the pervasive underreporting of gender-based violence."

The variety of scenarios uncovered in CBC's data include off-duty officers who allegedly abused their spouses, on-duty officers who had sex with vulnerable people, as well as cops who worked in sexual or domestic abuse units.

CBC's data set also revealed multiple cases where the victims — girlfriends, wives and partners of officers — were themselves serving in law enforcement. And like the sergeant from the GTA, they found themselves on the other side of the desk in a policing system they say is unsupportive and rife with professional conflict. 

• Exclusive CBC investigation: Paid to stay home

Reprimanded and muzzled

Kelly Donovan decided to become a police officer to effect change after leaving an abusive, common-law relationship with a southwestern Ontario officer.

"He had always kind of been controlling," said Donovan of their long-term relationship. "The abuse became physical and I had to eventually go to the police for help." 

But she said the officer who arrived talked her into "watering down" her statement.

"He kept saying, 'You need to understand the implications of what you're telling me,'" Donovan remembers.

I knew I had to do something about it. - Kelly Donovan, former police officer

No charges were laid against her ex. 

The experience galvanized Donovan, in part, to join a nearby police force. She became a constable with the Waterloo Regional Police Service in late 2010.

Six years into that job, she worried her employer wasn't consistent when it came to handling allegations against its own members.

"They weren't investigating their own people for domestic violence the way they were investigating members of the public," said Donovan. "I knew I had to do something about it." 

In 2016, Donovan took a day off work and presented her concerns to Waterloo's police services board. Her plan was to encourage the force to establish a new policy for conducting investigations involving fellow officers.

But within days, Donovan was reprimanded. The chief forbade her from appearing at future board meetings without the chief's permission, and she was put on administrative duties. 

According to a professional standards directive, the force alleged she had criticized the police service and divulged information about a case without permission. An internal investigation was launched into multiple disciplinary charges against Donovan, including breach of confidence and neglect of duty.

Police leadership maintains she broke the code of conduct. She saw it all as retaliation and said she was muzzled. 

Donovan resigned from the force 14 months later. The now former chief has said that Waterloo police are committed to building and modernizing a "strong workforce" where all members thrive, but the  legal battle between Donovan and the Waterloo force continues. 

Today, she's a vocal critic of the way police forces across Canada handle internal investigations. 

"When you look at transcripts from court hearings and things for domestic violence, the woman might have said to her partner, 'Stop doing this or I'm going to call the police,'" said Donovan. 

"And they'll say, 'I am the police. Do you think my buddies are going to come here and arrest me?'" 

'We hire from the human race'

CBC's research found that 36 per cent of officers accused of crimes involving gender-based violence were convicted. By contrast, the rate of criminal conviction was 58 per cent for other charges including drug trafficking, fraud, impaired driving and assault.

"It disheartens me for sure," said Jeff McGuire, executive director of the Ontario Association of Chiefs of Police, reacting to CBC's gender-based violence data. 

"But we hire from the human race," he said. "Keeping in mind that these are events that occur in the officers' private life."

In fact, CBC's data set does include some cases where the alleged incidents took place while officers were on the job, including:

• A Brockville officer was convicted in 2019 after reportedly having sex with a woman in the back of his police van while on duty. The woman was still under active police investigation at the time.
• A Toronto officer was sentenced to four years in prison in February for sexually assaulting the victim of a crime while the officer was on duty. 
• A case involving another Toronto officer who was charged in May 2023 alleges he had "unwanted sexual relations" with a woman who'd called police to report a domestic assault.

"To be revictimized by a police officer, I just think that's such a breach of trust, such a violation of the public's confidence in the criminal justice system and in policing specifically," said McNabb. 

'Really troubling'

Among the 158 suspensions tied to allegations of gender-based violence since 2013, CBC found close to one-third of the 53 criminally convicted officers returned to work after fulfilling their disciplinary requirements such as a conditional discharge. 

There's no rule against an officer getting back on the job after being convicted of a crime, unless they've been sentenced to imprisonment. 

Keri Lewis, executive director of Ottawa's Interval House, which offers shelter and support services for women and children fleeing abuse, said she's witnessed survivors of police abuse come through her doors.

"We know in the U.S. about 40 per cent of police families experience intimate partner violence, compared to about 10 per cent of the population," she said, noting a lack of Canadian statistics on the matter. 

To have the honour of entering people's lives in these incredibly vulnerable times, they should be held to a much higher standard. - Keri Lewis, Interval House

"To know that on a larger scale across the province that there are multiple officers who have been convicted of abuse or harassment, and that they've been allowed back into their roles as police officers in these positions of authority, is really troubling."

Like the police, Lewis said her employees are responsible for protecting vulnerable people. But her organization would not allow employees to return to work after a conviction involving gender-based violence.

"Officers are the same as us. They're meeting people at the most challenging, traumatic times of their lives," she said. "To have the honour of entering people's lives in these incredibly vulnerable times, they should be held to a much higher standard." 

CBC's research also found a small number of officers accused or convicted of gender-based violence have worked in domestic assault or sexual abuse units before or after their suspensions.

In March 2023, Const. Yourik Brisebois , an Ottawa Police Service (OPS) officer, was found guilty of two criminal charges including threatening to kill his ex while wielding a kitchen knife. She also works for the OPS.

Brisebois was suspended with pay for 18 months before returning to work. In the four years before the offences, he was a member of OPS's intimate partner violence unit.

"The cases that he was involved in are being reviewed by the Ottawa police. That's something that they've agreed to do," said Lewis, who pressured the OPS to initiate that review .

Exposed: Sexism within the Ottawa police

'Blue wall of silence'

Kate Puddister, an associate professor at the University of Guelph who focuses her research on criminal justice policy, said the culture of policing is part of the problem, allowing a "blue wall of silence" to pervade investigations involving other officers.  

"Concerns about fear, intimidation and retaliation are all increased when the perpetrator is a police officer, and these concerns don't disappear when the complainant or victim is a police officer themselves," she said.

Those concerns are shared by several male officers who were previously suspended for allegations of gender-based violence.

While none of the four sources agreed to speak on the record due to professional and legal concerns, they all told CBC they were unfairly treated when their own forces investigated them. 

Mark Baxter, president of the Police Association of Ontario, said his organization, which represents sworn officers belonging to 45 unions, supports everyone involved in these kinds of alleged crimes.

"We don't support violence against women and we don't think that gender-based violence is acceptable in society, and so we know that there are processes in place to deal with officers that are accused of those situations," said Baxter.

The GTA sergeant who reported her constable husband said in the aftermath of her case, the treatment she received from some police colleagues and leaders felt almost as bad as the abuse she suffered during her marriage.

In the days after her ex's arrest, she was reassigned to the domestic assault unit where she would have to review and investigate crimes against other women. 

Her close colleagues were also privy to her own sensitive information.

"It was awful in the office where everyone knows my business, where on the server there's naked pictures of me," she recalled. 

"It was so surreal," the officer said. "I'm telling my former subordinate all these intimate details." 

She remembers thinking she'd be better supported had she been shot or diagnosed with cancer. 

Her ex-husband was convicted of one criminal charge in 2020, while other charges were dropped. He was given a suspended sentence and two years of probation. The constable quit before his disciplinary hearing.

She, too, is away from her job — not because of anything she did, but because of all that's happened to her. 

The sergeant is on medical leave due to trauma and stress. 

"I gave extra. I stayed later. I did training videos and I did special presentations," she said about the job that she loved. "I did all the things and I thought it mattered, and then it didn't, and it was shocking."

ABOUT THE AUTHOR

Senior Reporter

Julie Ireton is a senior investigative reporter with CBC Ottawa. She's also the multi-award winning host of the CBC investigative podcasts, The Banned Teacher found at: cbc.ca/thebannedteacher and The Band Played On found at: cbc.ca/thebandplayedon You can reach her at [email protected]

With files from Valerie Ouellet

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