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Sexual Partners of Street-Based Female Sex Workers in St. Petersburg Russia: A Model of Partnership Characteristics as a Basis for Further Research

机译:基于街道的女性性工作者的性伴侣在圣彼得堡俄罗斯:伙伴关系特征模型作为进一步研究的基础

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摘要

Street-based sex work in Russia, as in many countries, carries with it a high risk for violence and the transmission of infectious diseases. The male partners of female sex workers are both cause and recipient of such risks. Because little is known about the men, we undertook a preliminary study to determine the feasibility of recruiting and interviewing them, develop typologies that describe partners, and derive hypotheses for further study and risk reduction intervention projects. We were able to conduct open-ended, qualitative interviews with street-based sex workers and, largely through these contacts, their male partners. To these data, we added interviews with social work and medical experts who engage with the sex workers. The text of interviews from 37 respondents were analyzed to identify commonly mentioned partner characteristics in five distinct domains: sociodemographics, behavioral patterns of the partners, motivations in seeking sex services, levels of partner engagement with the sex workers, and the social circumstances that moderate the engagement. Four of the five domains (all but sociodemographics) proved useful in identifying typologies that were best described as populated points in a matrix generated from the intersection of the four domains. The data were too limited to specify which of the points in the matrix are most common, but the points populated are useful in generating hypotheses for further study and in identifying potential avenues for risk reduction interventions.
机译:俄罗斯街道的性工作,如在许多国家,带来了暴力和传染病的高风险。女性性工作者的男性合作伙伴都是这种风险的原因和接受者。由于对男性众所周知,我们进行了初步研究,以确定招聘和面试的可行性,开发描述合作伙伴的类型,以及用于进一步研究和风险减少干预项目的假设。我们能够与街头的性工作者进行开放式的定性访谈,并主要通过这些联系人,他们的男性合作伙伴。对这些数据来说,我们添加了与与性工作者互动的社会工作和医学专家的访谈。分析了来自37名受访者的访谈文本,以确定五个不同域名的常见伙伴特征:合作伙伴的社会图表,行为模式,寻求性服务的动机,与性工作者的合作伙伴的程度,以及适度的社会环境订婚。五个域名(所有但社会图中的四个)证明是有用的,用于识别最佳描述的类型,这些类型是从四个域的交叉点生成的矩阵中的填充点。数据过于有限,指定矩阵中的哪个点是最常见的,但填充的点对于产生的假设可用于进一步研究以及识别风险降低干预措施的潜在渠道。

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