{"title":"描述亲密伴侣暴力治疗中的差异。","authors":"Çerağ Oğuztüzün, Mehmet Koyutürk, Günnur Karakurt","doi":"","DOIUrl":null,"url":null,"abstract":"<p><p>Exposure to Intimate Partner Violence (IPV) has lasting adverse effects on the physical, behavioral, cognitive, and emotional health of survivors. To this end, it is critical to understand the effectiveness of IPV treatment strategies in reducing IPV and its debilitating effects. Meta-analyses designed to comprehensively describe the effectiveness of treatments offer unique advantages. However, the heterogeneity within and between studies poses challenges in interpreting findings. Meta-analyses are therefore unlikely to identify the factors that underlie disparities in treatment efficacy. To characterize the effect of demographic and social factors on treatment effectiveness, we develop a comprehensive computational and statistical framework that uses Meta-regression to characterize the effect of demographic and social variables on treatment outcomes. The innovations in our methodology include (i) standardization of outcome variables to enable meaningful comparisons among studies, and (ii) two parallel meta-regression pipelines to reliably handle missing data.</p>","PeriodicalId":72181,"journal":{"name":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","volume":"2023 ","pages":"408-417"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283094/pdf/2326.pdf","citationCount":"0","resultStr":"{\"title\":\"Characterizing Disparities in the Treatment of Intimate Partner Violence.\",\"authors\":\"Çerağ Oğuztüzün, Mehmet Koyutürk, Günnur Karakurt\",\"doi\":\"\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Exposure to Intimate Partner Violence (IPV) has lasting adverse effects on the physical, behavioral, cognitive, and emotional health of survivors. To this end, it is critical to understand the effectiveness of IPV treatment strategies in reducing IPV and its debilitating effects. Meta-analyses designed to comprehensively describe the effectiveness of treatments offer unique advantages. However, the heterogeneity within and between studies poses challenges in interpreting findings. Meta-analyses are therefore unlikely to identify the factors that underlie disparities in treatment efficacy. To characterize the effect of demographic and social factors on treatment effectiveness, we develop a comprehensive computational and statistical framework that uses Meta-regression to characterize the effect of demographic and social variables on treatment outcomes. The innovations in our methodology include (i) standardization of outcome variables to enable meaningful comparisons among studies, and (ii) two parallel meta-regression pipelines to reliably handle missing data.</p>\",\"PeriodicalId\":72181,\"journal\":{\"name\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"volume\":\"2023 \",\"pages\":\"408-417\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10283094/pdf/2326.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science","FirstCategoryId":"1085","ListUrlMain":"","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/1/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
亲密伴侣暴力(IPV)会对幸存者的身体、行为、认知和情感健康产生持久的不良影响。为此,了解 IPV 治疗策略在减少 IPV 及其破坏性影响方面的有效性至关重要。旨在全面描述治疗效果的 Meta 分析具有独特的优势。然而,研究内部和研究之间的异质性给解释研究结果带来了挑战。因此,Meta 分析不太可能找出导致治疗效果差异的因素。为了描述人口和社会因素对治疗效果的影响,我们开发了一个全面的计算和统计框架,利用元回归来描述人口和社会变量对治疗结果的影响。我们在方法上的创新包括:(i) 对结果变量进行标准化,以便在不同研究之间进行有意义的比较;(ii) 两个并行的元回归管道,以便可靠地处理缺失数据。
Characterizing Disparities in the Treatment of Intimate Partner Violence.
Exposure to Intimate Partner Violence (IPV) has lasting adverse effects on the physical, behavioral, cognitive, and emotional health of survivors. To this end, it is critical to understand the effectiveness of IPV treatment strategies in reducing IPV and its debilitating effects. Meta-analyses designed to comprehensively describe the effectiveness of treatments offer unique advantages. However, the heterogeneity within and between studies poses challenges in interpreting findings. Meta-analyses are therefore unlikely to identify the factors that underlie disparities in treatment efficacy. To characterize the effect of demographic and social factors on treatment effectiveness, we develop a comprehensive computational and statistical framework that uses Meta-regression to characterize the effect of demographic and social variables on treatment outcomes. The innovations in our methodology include (i) standardization of outcome variables to enable meaningful comparisons among studies, and (ii) two parallel meta-regression pipelines to reliably handle missing data.