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Sexual Violence

Sexual violence refers to any sexual act, attempt to obtain a sexual act, unwanted sexual comments or advances, or acts to traffic, or otherwise directed, against a person's sexuality using coercion, by any person regardless of their relationship to the victim, in any setting.
It encompasses a wide range of sexual offences, including rape, sexual assault, child sexual abuse, and other forms of non-consensual sexual activity.
Sexual violence has significant physical, mental, and social consequences for individuals and communities, and is a major public health and human rights issue.
This MeSH term provides a comprehensive overview of the complex and multifaceted nature of sexual violence, its impacts, and the need for effective prevention and response strategies.

Most cited protocols related to «Sexual Violence»

1
Robust Sampling for Childhood Sexual Violence
Cited 41 times
The sample size for each country VACS is calculated based on an estimated prevalence of childhood sexual violence from existing data in each country (ie, the Demographic and Health Survey or other surveys), relative standard error and margin of error. Childhood sexual violence is used as the basis of power estimations because it is typically the least prevalent type of violence. This yields sample sizes that are more likely to be powered to detect and report results for the least prevalent violence type. The effective sample size is bolstered for the cluster design of the survey and adjusted for non-response, including household and individual response rates. The sample size for the VACS is robust, with a range of 891–7912 females and 803–2717 males in each country (table 1).
Weighting is used to obtain representative parameter estimates from survey data. It accounts for the probability that each respondent came into the sample, the differential effects of non-response and imperfect sampling frames that affect the composition of the sample.5 Final sample weights are calculated by (1) determining base weights to account for all steps of random selection that led to the sample of population members, (2) adjusting for non-response and (3) adjusting the final set of adjusted weights to the distribution of the population.
Finally, response rates are calculated using formulas from the American Association of Public Opinion Research.6 These rates are computed for the entire sample (both household and individual). Estimated individual-level eligibility rates are calculated separately for females and males of responding households. The final response rate is the multiplication of household and individual-level response rates. The response rates for the VACS range from 72.0% to 97.9% among females and 65.7% to 98.1% among males (table 1).
Nguyen K.H., Kress H., Villaveces A, & Massetti G.M. (2018). Sampling design and methodology of the Violence Against Children and Youth Surveys. Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention, 25(4), 321-327.
Publication 2018
Diet, Formula Eligibility Determination Females Households Males Reading Frames Sexual Violence
2
Developing a Common Factors Tool for Mental Health Care in Nepal
Cited 35 times

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Publication 2015
Community Health Workers Ethanol Health Personnel Mental Health Patients Primary Health Care Psychometrics Sexual Violence Therapeutics Vaginal Diaphragm Wellness Programs
3
Reproductive Coercion and Unintended Pregnancy
Cited 29 times

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Publication 2010
Condoms Contraceptive Methods Contraceptives, Oral Ethnicity Fear Forms Control Immigrants Infant Physical Examination Pregnancy Pressure Reproduction SERPINA3 protein, human Sexual Partners Sexual Violence
4
Conflict-related Trauma and Suicide Behavior
Cited 13 times
Ethics statement: Ethical approval for the study was granted by the University of Ulster ethical committee. Written informed consent was obtained from the participants in this study for their involvement in the research.
The NISHS was the largest epidemiological study of mental health in NI. A multi-stage, clustered, area probability household sample was drawn based on the structure and information from the 2001 NI census. The sample size was 4,340 and the response rate was 68.4%. Data was cleaned and missing data collected or imputed prior to the analysis. See Bunting et al. for further details of the sampling procedures and quality assurance strategies [9] (link). The NISHS survey instrument was administered in two sections; all participants completed Section 1, section 2 was then administered to respondents who met the criteria for any core disorder, an additional 50% of individuals who were subthreshold core disorder cases, and a 25% sample of all other individuals (n = 1,986). This sampling strategy enabled the computation of weights to adjust for differential selection for Section 2. Weights to minimize the effects of bias included information relating to sample selection, nonresponse, and poststratification factors such as age, sex, and geographical region [14] . The NI population characteristics at the midpoint of the data collection period were used in these weight calculations.
The survey instrument was the WMH Composite International Diagnostic Interview (WMH-CIDI) [13] . This is a comprehensive, fully structured interview for the assessment of mental disorders according to the ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines (ICD-10) [15] and DSM-IV criteria [16] .
Lifetime suicidal behaviour was assessed using three questions from the suicidality section in part two of the WMH-CIDI: “Have you ever seriously thought about committing suicide?”, “Have you ever made a plan for committing suicide?”, and “Have you ever attempted suicide?”.
Traumatic events were assessed in the PTSD section of part two of the WMH-CIDI. Participants were presented with 28 types of traumatic events and asked whether they had experienced them during their lifetime and if they endorsed a particular event, they were asked the age at which they first experienced this event type. The research team identified events that were presumed to be conflict-related, drawing upon a previous study of conflict in Lebanon [17] (link). Individuals were assigned to a conflict-related category if they experienced any one of the following events from 1968 onwards: combat experience, peacekeeper in a place of war, unarmed civilian in a place of war, civilian in a place of ongoing terror, refugee, kidnapped, man-made disaster, beaten by someone other than parents or partner, mugged or threatened with a weapon, witnessed someone being killed or seriously injured, purposely caused injury or death, or saw atrocities. The event types classified as non-conflict related included rape and sexual violence, death or illness of a loved one or diagnosis with a life threatening condition. It is also likely that a proportion of unexpected deaths and traumatic events involving loved ones could be associated with the NI conflict, however we did not categorise these event types as conflict-related. This is therefore likely to be a conservative estimation of conflict-related trauma. Mental disorders were assessed on the basis of DSM criteria [16] again using the WMH-CIDI.
Chi squared tests were used to assess whether the difference in proportions between categories were statistically significant. The association between traumatic event types and suicidal ideation, plan and attempt, controlling for the effects of any lifetime mental disorder, was examined using logistic regression. The reference category for the logistic regression was not having endorsed suicidal ideation (“seriously considered suicide”). The analysis incorporated weights to adjust for the differential selection for Section 2, sample selection, nonresponse, and poststratification factors, age, sex, and geographical region [14] . All analyses were implemented using STATA version 10.0 [18] .
O'Neill S., Ferry F., Murphy S., Corry C., Bolton D., Devine B., Ennis E, & Bunting B. (2014). Patterns of Suicidal Ideation and Behavior in Northern Ireland and Associations with Conflict Related Trauma. PLoS ONE, 9(3), e91532.
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Publication 2014
Atrocities Behavior Disorders Brassica rapa Diagnosis Disasters Households Injuries Mental Disorders Mental Health Parent Post-Traumatic Stress Disorder Refugees Sexual Violence Suicide Attempt Wounds and Injuries
5
Measuring Intimate Partner Violence and Unintended Pregnancies
Cited 11 times

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Publication 2013
Abuse, Physical Contraceptive Methods Ethnicity Infant Physical Examination Pregnancy Pressure Reproduction Reproductive Behavior Sadness Sexual Violence Woman

Most recents protocols related to «Sexual Violence»

1
Assessing Intimate Partner Violence
Individuals were asked about exposure to lifetime traumatic events including physical assault or abuse as an adult from an intimate partner and sexual assault or rape in adulthood, as described above. In addition, individuals also completed the Demographic and Health Survey (DHS) domestic violence module which captures four distinct domains of IPV: controlling behavior and emotional, sexual, and physical IPV [37 ]. This tool is a modified version of the Conflict Tactics Scale and asks respondents if they have experienced 15 separate behaviorally-specific types of IPV [38 (link)]. In our prior work, we noted discrepancies between reports of physical and sexual violence on the DHS domestic violence module and the modified version of the Life Events Checklist for DSM-5 used in the assessment of lifetime PTEs in this analysis [39 ]. Thus, we considered individuals who reported physical IPV with their most recent partner on the DHS domestic violence module, but did not report physical assault or abuse from a partner in adulthood on the PTE assessment to have experienced physical assault or abuse in adulthood from a partner in this analysis. Similarly, we considered individuals who reported sexual IPV with their most recent partner on the DHS domestic violence module but did not report sexual assault or rape as an adult on the PTE assessment to have experienced sexual assault or rape in adulthood.
Parcesepe A.M., Filiatreau L.M., Ebasone P.V., Dzudie A., Pence B.W., Wainberg M., Yotebieng M., Anastos K., Pefura-Yone E., Nsame D., Ajeh R, & Nash D. (2023). Prevalence of potentially traumatic events and symptoms of depression, anxiety, hazardous alcohol use, and post-traumatic stress disorder among people with HIV initiating HIV care in Cameroon. BMC Psychiatry, 23, 150.
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Publication 2023
Adult Brassica napus Domestic Violence Drug Abuse Emotions Menopause Physical Examination Sexual Assault Sexual Partners Sexual Violence
2
Differential Impacts of EMAP on IPV Subgroups
To examine the differential impacts of EMAP by baseline experience of IPV reported by women, subgroups of couples are constructed using two different methods. First, subgroups of couples are defined using the binary indicators of IPV experience in the last 12 months, for physical and sexual violence. Throughout the paper, we use the terms “physical and sexual IPV” and “physical and sexual violence” interchangeably. A couple is in the “physically violent” subgroup if the female partner of the male participant reported an instance of physical violence perpetrated by her intimate partner in the 12 months prior to baseline. Similarly, a couple is in the “physically non-violent” subgroup if the female partner reported at baseline that none of the instances of physical violence had occurred in the last 12 months. The same methodology is followed to form the subgroups of couples for sexual violence. The sexually and physically violent subgroups at baseline are not mutually exclusive.
Second, to explore distinct patterns of IPV experience, a Latent Class Analysis (LCA) was conducted to identify latent subgroups (i.e., latent classes) of couples exposed to physical and sexual violence at baseline. LCA is a method for identifying and understanding unobserved groups in a population [19 ]. In LCA models, classes are identified in a way where each class is distinct from one another and the individuals within classes have similar responses to the items included in the analysis [20 (link)]. Unlike the subgroups based on binary indicators, LCA considers co-occurrence of different types of violence. Researchers are increasingly using LCA to identify subgroups of IPV exposure. Previous work relying on LCA to identify distinct classes of violence have explored the association of these classes with mental health outcomes [20 (link)–23 (link)], paid work disruptions due to IPV [24 (link)], and perpetration of IPV [9 (link)].
Physical and sexual IPV experience as reported by female partners are measured with 7 physical violence items and 3 sexual violence items (Table 1). We used these 10 IPV items to fit a series of latent class models with various number of classes (from 2 to 4). The baseline frequency of each item used is reported in S1 Table 1 in the S1 Appendix. The best-fitting model is detected by selecting the model with the largest log-likelihood, and the smallest values of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and examining the entropy measure (See S1 Table 2 in the S1 Appendix).
Having defined these subgroups of couples based on the female partner’s experience of IPV at baseline, we conduct an intention-to-treat analysis of the impacts of EMAP separately for each subgroup of couples identified through both methods using ordinary least squares (OLS) and linear probability (for binary outcomes) models. Our findings are robust to using logistic models. Results are available upon request. We estimate adjusted models in which we control for the following baseline characteristics: men and women’s age and education, household size, and the language of the interview. For all analyses, standard errors are clustered at the site level in the regressions.
Gurbuz Cuneo A., Vaillant J., Koussoubé E., Pierotti R.S., Falb K, & Kabeya R. (2023). Prevention, Cessation, or harm reduction: Heterogeneous effects of an intimate partner violence prevention program in eastern Democratic Republic of the Congo. PLOS ONE, 18(3), e0282339.
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Publication 2023
Abuse, Physical Entropy Households Males Mental Health Physical Examination Population Group Sexual Violence Woman
3
Substance Use and Violence Factors
Frequencies, percentages and medians were used to describe respondents’ characteristics and substance use risk levels (i.e. low, moderate, high). Logistic regression modelling was used to examine significant factors associated with high-risk substance use (ASSIST score ≥27). Factors included in the analyses were the socio-demographic characteristics, Accra migration and homelessness variables, social stigma, and self-reported physical and mental health problems. Two measures of violence (i.e. physical/emotional and sexual) were also included. Physical and emotional violence were merged into one measure (i.e. experience of physical or emotional violence, dichotomised as Yes = 1 and No = 0) due to high correlation (r = .96).
Separate multivariable logistic regression analyses were conducted for high-risk use of alcohol, cocaine, and cannabis due to their particularly high prevalence in both the general Ghanaian population and this study’s sample. Variable selection techniques consisted of two separate logistic regression analyses as follows:
However, self-blame (a sub-category of social stigma) and presence of mental health problem were included apriori in all analysis. International literature shows that that people engage in problematic substance use as a coping strategy for stress, trauma, and stigma from violence [46 (link), 47 (link)]. Similarly, there is an established relationship with substance use in the homeless population [7 (link)].
Adjusted Odds Ratios (AORs) with 95% confidence intervals (CIs) were estimated as the measure of association between the explanatory and outcome variables. All statistical analyses were conducted in Stata version 14.0 (Stata Corp, College Station, TX, USA). All variables with a p-value≤.05 were considered significant. Multicollinearity was checked after the regressions. Interactions were examined between gender and various measures of violence, as well as between physical/emotional and sexual violence; none of them were statistically significant (S1 Table).
Osei Asibey B., Marjadi B, & Conroy E. (2023). Alcohol, tobacco and drug use among adults experiencing homelessness in Accra, Ghana: A cross-sectional study of risk levels and associated factors. PLOS ONE, 18(3), e0281107.
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Publication 2023
Cannabis Cocaine Emotions Gender Mental Health Persons, Homeless Physical Examination Sexual Violence Substance Use Wounds and Injuries
4
Expanded Trauma Exposure Assessment for Filipino Migrants
A 24-item adaptation of the 17-item LEC (Weathers et al., 2013a ) was used in this study to assess PTE exposure. The LEC-5 is based on DSM-5 Criterion A of PTEs which are defined as: individuals personally experienced, witnessed, learned about, or experienced the event it as part of their occupation that caused serious injury, threatened death, actual death, or sexual violence (American Psychiatric Association, 2013 ). The adaptation added seven items that cover PTEs relevant to Filipino migrant workers based on a qualitative study (Hall et al., 2019a (link)). Participants responded on a 5-point scale: having personally experienced, witnessed, learned about the event, experienced it as part of their occupation, or never exposed. The last item asked participants to indicate their worst PTE, and subsequent PTSD assessment was completed in reference to this index trauma.
Mou N.L., Lei W.K., Balaji S., Contractor A.A., Latkin C.A, & Hall B.J. (2023). The association between posttraumatic disorder symptoms and addictive behaviours in Macao within a sample of female Filipino migrant workers: a network analysis. European Journal of Psychotraumatology, 14(1), 2178764.
Publication 2023
Acclimatization Injuries Migrant Workers Post-Traumatic Stress Disorder Sexual Violence Wounds and Injuries
5
Sampling Pregnant Mothers for Sexual Violence
The single population proportion formula was used to determine the sample size with the following assumptions (n = z2 p (1−p)/d2), where z is the normal standard deviation set at 1.96, the confidence level is specified at 95%, the margin of error (d) is 5%, a nonresponse rate of 10%, and a prevalence of sexual violence (p) of 23.7% from a previous study conducted in southeast Oromia (17 (link)). The calculated sample size was 306. Proportional allocation to each health facility was done by using the number of pregnant mothers who received ANC in that public health institution based on the number of women who received ANC in monthly reports before data collection. After that systematic random sampling procedure was used to select ANC followers. The sampling interval (K) was gained by dividing the expected total number of monthly ANC service users 1,090 (N) by the number sample size 306 (n) at each data collection site. Every 3rd pregnant mother was selected till the required sample size was achieved.
Abegaz M.Y., Muche H.A., Aynalem G.L., Anteneh T.A., Tibebu N.S., Gedef G.M, & Mengstie A.S. (2023). Prevalence of sexual violence and associated factors among women attending antenatal care in Debre Markos at public health institutions in north-west Ethiopia, 2021. Frontiers in Global Women's Health, 4, 1009272.
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Publication 2023
ANC 1 Mothers Sexual Violence Woman

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More about "Sexual Violence"

Sexual Assault, Rape, Sexual Abuse, Non-Consensual Sexual Activity, Sexual Offences, Sexual Trauma, Intimate Partner Violence, Domestic Violence, Gender-Based Violence, Victimization, Consent, Coercion, Exploitation, Power Dynamics, Trauma-Informed Care, PTSD, Mental Health, Public Health, Human Rights, Prevalence, Risk Factors, Prevention, Intervention, Treatment, Rehabilitation, Quantitative Analysis, Qualitative Research, SPSS, Stata, SAS, Data Collection, Data Analysis, Reporting, Reproducibility, Accuracy.
The topic of sexual violence encompasses a wide range of non-consensual sexual acts, including rape, sexual assault, child sexual abuse, and other forms of unwanted sexual activity.
It is a complex and multifaceted issue that has significant physical, mental, and social consequences for individuals and communities.
Sexual violence can occur in any setting and is often perpetrated by someone known to the victim, such as an intimate partner or family member.
Effective prevention and response strategies are crucial to addressing this public health and human rights issue.
These may involve trauma-informed care, mental health support, and community-based interventions.
Quantitative and qualitative research, facilitated by statistical software like SPSS, Stata, and SAS, plays a key role in understanding the prevalence, risk factors, and impacts of sexual violence, as well as evaluating the effectiveness of interventions.
By leveraging the power of AI-driven protocol optimization, researchers can enhance their sexual violence studies, identifying the best protocols and products from the literature, preprints, and patents.
This can improve the reproducibility and accuracy of their work, ultimately contributing to more effective prevention and response efforts.