Victim Items Research Articles

Assessing the Reversed Gender Gap in the Swedish Crime Survey: Reasons Why Young Women Report Higher Victimization Rates Than Young Men During the Period 2009 to 2016

Self-reported victimization among young women in the Swedish Crime Survey increased during the early- to mid-2010s, resulting in a gender gap, with young women reporting 10% to 15% higher victimization rates than young men. In this study, we use multi-group confirmatory factor analysis to assess whether the increase in victimization rates is due to declines in measurement equivalence across different cohorts of young women during the period 2009 to 2016. The results show cohort differences in how young women interpret the victimization items in the Swedish Crime Survey, especially with regard to the item about sexual victimization. The results show that the increasing victimization rates are likely the result of changes in perceptions about sexual offences, rather than an actual increase.

Expanding the Sexual Experiences Survey to Include Technology Facilitated Sexual Exploitation

ABSTRACT The Sexual Experiences Survey is the most widely used measure of sexual assault victimization and perpetration among college students. To improve comprehensiveness and inclusiveness, the current revision of the victimization items of the SES (SES-V; Koss et al., 2024) included the addition of a module focusing on Technology Facilitated Sexual Exploitation (TFSE). The current paper outlines why the inclusion of items related to TFSE was necessary, and describes the development of the items making up this module of the SES-V. The module consists of 10 items that map onto seven domains: receipt of sexual materials, target of unwanted sexual comments, threatened sharing of sexually explicit images, actual sharing of sexually explicit images, target of sexual solicitation, threatened sharing of images of sexual exploitation, and actual sharing of images of sexual exploitation. Consistent with how the other types of sexual exploitation are assessed in the SES-V (Koss et al., 2024), respondents indicate how many times they have experienced these events since their 14th birthday and use an 11-point response format ranging from 0 to 10 or more times per experience. The addition of the new module on TFSE to the SES-V will ensure researchers assess both in-person and cyber modalities of sexual exploitation.

The longitudinal relationship between bullying victimization and depressive symptoms for middle school students: A cross-lagged panel network analysis

BackgroundPrevious research about the relationship between bullying victimization and adolescent depressive symptoms was mostly based on latent variable modeling. This study, instead, applied item-level analysis to explore the cross-sectional relationship and longitudinal development between bullying victimization and adolescent depressive symptoms with network models. MethodsThis study used Olweus Bully/Victim Questionnaire and Children's Depression Inventory to collect data. A total of 1911 middle school students (55.2 % female; Mage = 12.98 ± 0.60 at T1) completed measures on four occasions at 6-month intervals. Nine network analysis models were employed to better understand the relationship between variables. Results(1) “Being threatened or intimidated” was the most influential bullying behavior within bullying victimization items; (2) “being excluded”, “being spoken ill of” and “negative mood” were the bridge items between bullying victimization and adolescent depressive symptoms; (3) the most influential bullying victimization item on adolescent depressive symptoms was “being robbed or blackmailed” for short-term development and “being excluded” for long-term development. While the most affected depressive symptom by bullying victimization was “anhedonia” for short-term development and “negative mood” for long-term development. LimitationsSelf-report measure is adopted for all variables in the study, and there may be some deviation. Due to the questionnaires, the items of bullying behaviors and depressive symptoms included in the network analysis are limited. ConclusionsFrom the item level, this study found more specific relationships between bullying victimization and adolescent depressive symptoms. These findings highlight depressed mood and anhedonia are depressive symptoms that should be more paid attention to in clinical intervention for bullying victims.

Resilience mediates the effect of peer victimization on quality of life in Chongqing adolescents: from a perspective of positive childhood experiences.

Peer victimization is a harmful experience that contributed to one's psychological problems, physical health deterioration, and so on. Quality of life (QoL) is an important indicator of adolescent health assessment. To identify potential pathways of positive experiences in preventing peer victimization's detrimental effects and then provide intervention ideas for adolescent health, this study was conducted to examine the relationship between peer victimization and QoL in Chongqing adolescents and discover whether resilience plays a mediating role and positive childhood experiences (PCEs) act as a moderating role in the relationship. Data were the first follow-up of a cohort study conducted in four complete middle schools in two districts of Chongqing, China. Self-designed peer victimization items, the Connor-Davidson Resilience Scale, the Adolescent Quality of Life Scale, and the Benevolent Childhood Experiences Scale were used. We investigated the differences and correlations in peer victimization, QoL, and resilience between the two PCEs groups. Mplus version 8.3 was used to analyze the mediating role of resilience and the moderating role of PCEs in peer victimization and QoL. Peer victimization, resilience, and QoL differed between the two PCEs groups (P < 0.001). Peer victimization negatively correlated with QoL and resilience, while resilience positively correlated with QoL (P < 0.001). In the models with total QOL as the dependent variable, the indirect effect was -0.431 (8.08% of the total effect) in the low-PCEs group vs. -2.077 (41.97% of the total effect) in the high-PCEs group. In the models with four dimensions of QOL as the dependent variable, the indirect effects ranged from -0.054 to -0.180 (6.07-12.95% of the total effects) in the low-PCEs group and from 0.295 to -0.823 in the high-PCEs group (35.89-68.76% of the total effects). Both total and indirect effects were significant (P < 0.05). In addition, the differences in indirect effects were significant between the two PCEs groups (P < 0.05), while differences in total and direct effects were almost not apparent. Resilience partially mediated the effect of peer victimization on QoL in Chongqing adolescents, and PCEs moderated this mediation. Schools, families, and society should focus on resilience intervention and prioritize the enhancement of PCEs for improving adolescent QoL.

Efficient privacy preserving algorithms for hiding sensitive high utility itemsets

Utility-driven mining is a powerful data mining technique that aims to extract significant and valuable knowledge from different kinds of datasets. Nevertheless, analyzing datasets with sensitive or private information may raise security and privacy concerns. To balance utility maximization and privacy preservation, Privacy Preserving Utility Mining (PPUM) has been presented. The primary objective of PPUM algorithms is to conceal the sensitive knowledge that can be found via the application of utility mining algorithms to sensitive data. However, the current PPUM literature shows a paucity of privacy preserving algorithms scalable and efficient enough to handle large and dense datasets. Based on this perspective, this paper proposes three heuristic-based algorithms, namely Selecting the Most Real item sensitive utility First (SMRF), Selecting the Least Real item sensitive utility First (SLRF), and Selecting the most Desirable Item First (SDIF), to efficiently conceal all Sensitive High utility Itemsets (SHIs) while mitigating the projected detrimental impact on the non-sensitive information. The proposed algorithms rely on a novel concept, called Real Item Sensitive Utility (RISU), to effectively select a definite victim item for each SHI during the whole sanitization process. Furthermore, a new sensitive dataset sorting technique is proposed to reduce the time needed to find suitable transactions for sanitization. Through comprehensive experimental evaluations with state-of-the-arts, the viability of the proposed three algorithms was testified. The acquired findings clearly demonstrate the efficacy of the proposed algorithms in terms of reducing the sanitization time and side effects.

Adversarial Machine Learning in Recommender Systems

Recommender systems are ubiquitous. Our digital lives are influenced by their use when, for instance, we select the news to read, the product to buy, the friend to connect with, and the movie to watch. While enormous academic research efforts have been mainly focused on getting high-quality recommendations to reach maximum user satisfaction, little effort has been devoted to studying the integrity and security of these systems. Is there an underlying relationship between the characteristics of the historical user-item interactions and the efficacy of injection of false users/feedback strategies against collaborative models? Can public semantic data be used to perform attacks more potent in raising the recommendability of victim items? Can a malicious user poison or evade the image data of visual recommenders with adversarial perturbed product images? Is the family of model-based recommenders more vulnerable to multi-step gradient-based adversarial perturbations? Furthermore, is the adversarial training robustification still effective in the last scenario? Is this training defense influencing the beyond-accuracy and bias performance? This dissertation intends to pave the way towards more robust recommender systems, beginning with understanding how to robustify a model, what is the cost of robustness in terms of reduction of recommendation accuracy, and which are the novel adversarial risks of modern recommenders. This thesis, getting inspiration from the literature on the security of collaborative models against the insertion of hand-engineered fake profiles and the recent advances of adversarial machine learning methods in other research areas like computer vision, contributes to several directions: (i) the proposal of a practical framework to interpret the impact of data characteristics on the robustness of collaborative recommenders [Deldjoo et al., 2020], (ii) the design of powerful attack strategies using publicly available semantic data [Anelli et al., 2020], (iii) the identification of severe adversarial vulnerabilities of visual-based recommender models where adversaries can break the recommendation integrity by pushing products to the highest recommendation positions with a simple and human-imperceptible perturbation of products' images [Anelli et al., 2021b], (iv) the proposal of robust adversarial perturbation methods capable of completely breaking the accuracy of matrix factorization recommenders [Anelli et al., 2021a], and (v) a formal study that examines the effects of adversarial training in reducing the recommendation quality of state-of-the-art model-based recommenders Anelli et al. [2021c]. Awarded by: Politecnico di Bari, Bari, Italy on 24 January 2022. Supervised by: Tommaso DI Noia. Available at: https://iris.poliba.it/retrieve/dd89f8a6-faa4-ccdd-e053-6605fe0a1b87/Adversarial_Machine_Learning_in_Recommender_Systems.pdf.

Novel Approach to Hide Sensitive Association Rules by Introducing Transaction Affinity

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language.

Validity of a three-item dating abuse victimization screening tool in a 11–21 year old sample

BackgroundDating abuse (DA) is prevalent and consequential, but no brief DA screening tools are available for use in pediatric or other settings. This study was designed to determine the sensitivity, specificity, and predictive values of the MARSHA-C, which is a three-item DA victimization screening tool.MethodsThe participants were 224 U.S. youth ages 11–21 years old (20% male, 77% female, 3% non-binary gender). Youth completed an online questionnaire about adolescent relationship abuse. The survey included the Measure of Adolescent Relationship Harassment and Abuse (MARSHA), which is a comprehensive DA measurement instrument normed on a nationally representative sample. Of 34 DA victimization items from the MARSHA, the three most prevalent items were hypothesized to have good predictive validity of the full scale score as a brief, screening version (MARSHA-C). The sensitivity, specificity, positive predictive value, and negative predictive value of the MARSHA-C to identify victims of DA was calculated.ResultsUsing the MARSHA as the reference standard, the cutpoint of 1 on the MARSHA-C screening tool was identified as optimal. The MARSHA-C had a sensitivity of 84%, a specificity of 91%, and positive predictive value of 91%. Thus, for youth who endorse ≥ 1 MARSHA-C items, there is a 91% probability that they have experienced DA in the past year. Exploratory analyses by demographic subgroups suggest that the predictive validity of the MARSHA-C is approximately equivalent for females and males, younger and older adolescents, Asian, Black, Latinx, Multiracial and White youth, and heterosexual and lesbian, gay, and bisexual youth.ConclusionsThe MARSHA-C can be used to detect DA among 11–21-year-old youth via online surveys for research purposes, or in clinical care settings to facilitate proactive patient counseling or parent-oriented anticipatory guidance.

Hiding Sensitive High Utility and Frequent Itemsets Based on Constrained Intersection Lattice

Abstract Hiding high utility and frequent itemset is the method used to preserve sensitive knowledge from being revealed by pattern mining process. Its goal is to remove sensitive high utility and frequent itemsets from a database before sharing it for data mining purposes while minimizing the side effects. The current methods succeed in the hiding goal but they cause high side effects. This paper proposes a novel algorithm, named HSUFIBL, that applies a heuristic for finding victim item based on the constrained intersection lattice theory. This algorithm specifies exactly the condition that allows the application of utility reduction or support reduction method, the victim item, and the victim transaction for the hiding process so that the process needs the fewest data modifications and gives the lowest number of lost non-sensitive itemsets. The experimental results indicate that the HSUFIBL algorithm achieves better performance than previous works in minimizing the side effect.

Efficient algorithms for victim item selection in privacy-preserving utility mining

High-utility itemset mining has evolved as an essential and captivating research topic. It aims to extract the patterns/itemsets having high utility value; hence, they are called high utility itemsets (HUIs). From a business perspective, a utility can be the benefit associated with the sale of a particular item or the usefulness or satisfaction that a customer experiences from a product. The economic utilities are helpful to evaluate the drivers behind a customer’s purchase decision. The advances in information technology have enabled us to access the datasets related to various domains like health care, stock market, market-basket, education and bioinformatics. Companies strive to increase the utility value of their products and share their customer’s transactions data to extract high utility patterns to achieve global customer insights. However, this can lead to massive security and privacy risk if their competitors misuse the patterns that can disclose their confidential information. Privacy-preserving utility mining (PPUM) is a branch of privacy-preserving data mining (PPDM) that presents various algorithms which intend to hide sensitive high utility itemsets (SHUIs) and maintain a balance between utility-maximizing and privacy-preserving. In this paper, two SHUIs hiding algorithms, MinMax and Weighted, are proposed with three variants of each algorithm. Experiments on various datasets show that proposed algorithms perform better than the existing SHUIs hiding algorithms as fewer distortions of non-sensitive knowledge occur. This study uses six performance evaluating metrics to assess the proposed algorithms against compared algorithms.

Longitudinal Associations Between Sexual Victimization and Substance Misuse Among High School Youth in Colorado.

There are consistently high rates of sexual victimization and substance misuse among youth in the United States. Although there is a known relation between sexual victimization and substance use, there is a gap in the research regarding the magnitude and temporality of these associations. This study examined whether latent classes of sexual victimization and help-seeking attitudes longitudinally predict intentions of future substance misuse 7-10 months later. Students from nine Colorado high schools (N = 533; M = age 16 years) completed surveys across two consecutive school semesters. Latent class analysis was used to identify classes of students who experienced at least one form of sexual victimization (e.g., sexual harassment, unwanted sexual contact) according to 13 sexual victimization items, and level of positive attitudes regarding help-seeking for sexual victimization. Classes were compared on demographic characteristics and for distal outcomes of likelihood of future substance misuse (cigarettes, alcohol, cannabis, prescription drugs, and electronic vaping products) using latent class regression, controlling for previous intentions to use. At Time 1, four classes of sexual victimization were identified with two main classes for comparison: low odds of experiencing sexual victimization (60.1% of sample) and high odds of endorsing all forms of sexual victimization (7.7% of sample). The high sexual victimization class had higher proportions of male and transgender students, compared to other classes. At Time 2 (7-10 months later), students in the high sexual victimization class reported a significantly greater likelihood of future cigarette (p = .017) and prescription drug misuse (p = .007) when compared to the low sexual victimization class. There was no evidence that having higher positive attitudes towards help-seeking resulted in lower intentions to use substances in the future. These findings highlight that addressing sexual violence in prevention programming may have crossover effects of reducing substance misuse and other forms of violence among youth.

MR-OVnTSA: a heuristics based sensitive pattern hiding approach for big data

This paper presents a novel ‘MapReduce Based Optimum Victim Item and Transaction Selection Approach (MR-OVnTSA)’ that provides a feasible and intelligent solution for protecting sensitive frequent itemsets present in big data. The approach advocates to resolve the captious challenges, existing knowledge hiding algorithms are encountering. The proposed solution optimally minimizes the side effect of hiding process on non-sensitive information, and maintains a balance between knowledge and privacy as well as handles the exponential growth in data volume efficiently. The algorithm plugs the most optimum item and transaction as victim, by intelligently analyzing their coverage value i.e. it chooses one with maximal impact on sensitive knowledge but minimal on non-sensitive information. Further, the MapReduce version of the proposed scheme resolves the issue of non-feasibility by processing large-scale data (big data) in a parallel fashion. Experiments have been demonstrated over real and synthetically generated large-scale datasets. Results evince that the proffered scheme is much more efficient and maintains the balance between the privacy preservation, data quality maintenance, and CPU time, when dealing with large voluminous big datasets compared to existing knowledge hiding techniques.

VIDPSO: Victim item deletion based PSO inspired sensitive pattern hiding algorithm for dense datasets

Collaborative frequent itemset mining involves analyzing the data shared from multiple business entities to find interesting patterns from it. However, this comes at the cost of high privacy risk. Because some of these patterns may contain business-sensitive information and hence are denoted as sensitive patterns. The revelation of such patterns can disclose confidential information. Privacy-preserving data mining (PPDM) includes various sensitive pattern hiding (SPH) techniques, which ensures that sensitive patterns do not get revealed when data mining models are applied on shared datasets. In the process of hiding sensitive patterns, some of the non-sensitive patterns also become infrequent. SPH techniques thus affect the results of data mining models. Maintaining a balance between data privacy and data utility is an NP-hard problem because it requires the selection of sensitive items for deletion and also the selection of transactions containing these items such that side effects of deletion are minimal. There are various algorithms proposed by researchers that use evolutionary approaches such as genetic algorithm(GA), particle swarm optimization (PSO) and ant colony optimization (ACO). These evolutionary SPH algorithms mask sensitive patterns through the deletion of sensitive transactions. Failure in the sensitive patterns masking and loss of data have been the biggest challenges for such algorithms. The performance of evolutionary algorithms further gets degraded when applied on dense datasets. In this research paper, victim item deletion based PSO inspired evolutionary algorithm named VIDPSO is proposed to sanitize the dense datasets. In the proposed algorithm, each particle of the population consists of n number of sub-particles derived from pre-calculated victim items. The proposed algorithm has a high exploration capability to search the solution space for selecting optimal transactions. Experiments conducted on real and synthetic dense datasets depict that VIDPSO algorithm performs better vis-a-vis GA, PSO and ACO based SPH algorithms in terms of hiding failure with minimal loss of data.

Privacy-preserving in association rule mining using an improved discrete binary artificial bee colony

Association Rule Hiding (ARH) is the process of protecting sensitive knowledge using data transformation. Although there are some evolutionary-based ARH algorithms, they mostly focus on the itemset hiding instead of the rule hiding. Besides, unstable convergence to the global optimum solution and designing long solutions make them inappropriate in reducing side effects. They use the basic versions of evolutionary approaches, resulting in inappropriate performance in ARH domain where the search space is large and the algorithms easily get trapped in local optima. To deal with these problems, we propose a new rule hiding algorithm based on a binary Artificial Bee Colony (ABC) approach which has good exploration. However, we improve the binary ABC algorithm to enhance its poor exploitation by designing a new neighborhood generation mechanism to balance between exploration and exploitation. We called this algorithm Improved Binary ABC (IBABC). IBABC approach is coupled with our proposed rule hiding algorithm, called ABC4ARH, to select sensitive transactions for modification. To choose victim items, ABC4ARH formulates a heuristic. The performance of ABC4ARH algorithm on the side effects is demonstrated using extensive experiments conducted on five real datasets. Furthermore, the effectiveness of IBABC is verified using the uncapacitated facility location problem and 0–1 knapsack problem.

Improving Provider Readiness for Intimate Partner Violence Screening.

Intimate partner violence (IPV) is a significant public health issue. Healthcare providers (e.g., nurses, advanced practice nurses, physicians, social workers) have a unique opportunity to prevent and reduce IPV through screening and referral. The objective of this project was to determine the impact of education and a brief screening tool integrated into the electronic medical record (EMR) on readiness to screen for IPV. An intervention was implemented that included the EMR integration of a screening tool, creation of an automated resource telephone system and healthcare provider IPV screening and response education. Readiness for screening was evaluated pre- and postintervention using the Domestic Violence Health Care Provider Survey Scale (DVHCPSS), which is scored cumulatively and by each of six domains. An unpaired Student's t test was performed. Mean age (31-40years of age) and years of clinical practice (11-15years) was the same for pre- (n=96) and postintervention (n=83) survey respondents. There was an overall significant increase in screening readiness (p=.003) with significant improvement in "professional role resistance/fear of offending the patient" (p<.0001), "blame victim items" (p=.0029), "perceived self-efficacy" (p=.0064), and "victim/provider safety" (p=.003). Adopting and integrating a validated IPV screening tool into the EMR combined with education was associated with an improvement in overall readiness for IPV screening. Reducing and preventing IPV through universal screening and referral can be accomplished by embedding a standardized readily accessible validated IPV screening tool in the EMR.

A Methodological Study of Order Effects in Reporting Relational Aggression Experiences.

Unlike the overt nature of physical aggression, which lends itself to simpler and more direct methods of investigation, the often-masked nature of relational aggression has led to difficulties and debate regarding the most effective tools of study. Given concerns with the accuracy of third-party relational aggression reports, especially as individuals age, self-report measures may be particularly useful when assessing experiences with relational aggression. However, it is important to recognize validity concerns-in particular, the potential effects of item order presentation-associated with self-report of relational aggression perpetration and victimization. To investigate this issue, surveys were administered and completed by 179 young adults randomly assigned to one of four survey conditions reflecting manipulation of item order. Survey conditions included presentation of (a) perpetration items only, (b) victimization items only, (c) perpetration items followed by victimization items, and (d) victimization items followed by perpetration items. Results revealed that participants reported perpetrating relational aggression significantly more often when asked only about perpetration or when asked about perpetration before victimization, compared with participants who were asked about victimization before perpetration. Item order manipulation did not result in significant differences in self-reported victimization experiences. Results of this study indicate a need for greater consideration of item order when conducting research using self-report data and the importance of additional investigation into which form of item presentation elicits the most accurate self-report information.

An Efficient Rule-Hiding Method for Privacy Preserving in Transactional Databases

One of the obstacles in using data mining techniques such as association rules is the risk of leakage of sensitive data after the data is released to the public. Therefore, a trade-off between the data privacy and data mining is of a great importance and must be managed carefully. In this study an efficient algorithm is introduced for preserving the privacy of association rules according to distortion-based method, in which the sensitive association rules are hidden through deletion and reinsertion of items in the database. In this algorithm, in order to reduce the side effects on non-sensitive rules, the item correlation between sensitive and non-sensitive rules is calculated and the item with the minimum influence in non-sensitive rules is selected as the victim item. To reduce the distortion degree on data and preservation of data quality, transactions with highest number of sensitive items are selected for modification. The results show that the proposed algorithm has a better performance in the non-dense real database having less side effects and less data loss compared to its performance in dense real database. Further the results are far better in synthetic databases in compared to real databases.

Factor Structure and Validation of the Controlling Behaviors Scale–Revised and Revised Conflict Tactics Scale

Recently, more attention is being paid to controlling behaviors within a continuum of intimate partner violence and abuse. However, it is unclear whether current scales are sufficiently valid to measure such behaviors. The current study assessed the factor structure and reliability of the revised Controlling Behaviors Scale (CBS-R) and the revised Conflict Tactics Scale (CTS2). Data were gathered from a U.K. general population sample ( N = 405). Confirmatory factor analyses were carried out on the CTS2 and the CBS-R, for both perpetration and victimization items, using the weighted least squares estimation with mean adjustment method. Multiple factor models were confirmed in the analysis of the CBS-R and CTS2 for perpetration and victimization items. Reliabilities for the factors were satisfactory across both scales. This is the first validation of the factor structure of the CBS-R and the findings suggests that this a valid and reliable scale for measuring controlling behaviors.

Improving Provider Readiness for Intimate Partner Violence Screening [35G

INTRODUCTION: Intimate Partner Violence (IPV) is a significant public health issue. Around 30% of American women yearly experience IPV with a lifetime incidence as high as 50%. An interprofessional team collaborated to increase and standardize IPV screening of women presenting for outpatient obstetric and gynecologic care through integration of a validated IPV screening tool into the electronic medical record (EMR). METHODS: The team evaluated provider readiness for IPV screening pre- and post- intervention through the use of The Domestic Violence Health Care Provider Survey Scale (DVHCPSS). An intervention was implemented that included the EMR integration of a validated IPV screening tool, the creation of an automated resource telephone system, and the education of providers regarding screening and response. The DVHCPSS is scored cumulatively for an overall score and may also be divided into six interest areas. Pre- and post-intervention DVHCPSS scores were compared using an independent Student t-test. RESULTS: Mean age (31 to 40) and years of clinical practice (11 to 15) was the same for both pre- and post-intervention survey respondents. There was an overall significant improvement in provider readiness (P=.003) with specific areas of significant improvement in “Professional Role Resistance/Fear of Offending the Patient” (P &lt; .0001), “Blame Victim Items” (P=.0029), and “Perceived Self Efficacy” (P=.0064). CONCLUSION: Adopting and integrating a validated IPV screening tool into the EMR paired with provider education was associated with an improvement in provider readiness for IPV screening. Ultimately, this aims to be a blueprint for IPV screening and referral across this community and others.

Self-report measures that do not produce gender parity in intimate partner violence: A multi-study investigation.

OBJECTIVE: Gender patterns in intimate partner violence (IPV) remain a controversial topic. Some self-report measures produce gender parity in IPV rates. However, other self-report surveys do produce gender parity, nor do arrests, reports to law enforcement, homicide data, helpseeking data, or witness reports. This methodological inconsistency is still poorly understood. The objective of these studies is to explore the effects of item wording on gender patterns for victimization reports in a range of samples. METHOD: In Study 1, 238 undergraduates were randomly assigned either the standard Conflict Tactics Scales (CTS) physical victimization items or a version which changed the partner-specific wording to generic wording (Someone instead of My partner), with perpetrator information collected in follow-up. Studies 2 and 3 compared the standard approach to items with stems intended to reduce false positives (either Not including horseplay or around. . or When my partner was angry. .), among 251 college students and 98 agency-involved women, respectively. Study 4 implemented the not joking alternative from Study 3 in a large rural community sample (n = 1,207). RESULTS: In Studies 1 and 2, significant Wording × Gender analyses indicated that some item wordings yielded higher rates of female than male victimization. Study 3 showed similar patterns across forms for highly victimized women. Study 4 found higher female than male victimization for a new scale and every item. CONCLUSION: The CTS and similar behavioral checklists are unusual in their inattention to false positives. Self-report measures designed to minimize false positives produce results consistent with other IPV methodologies; that is, they do demonstrate gender parity. The Partner Victimization Scale, described here, can be used when a scale that has multimethod convergence with other IPV methodologies is desired. ©2016 American Psychological Association Language: en