A network-individual-resource model for HIV prevention.

A network-individual-resource model for HIV prevention.
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DOI:
10.1007/s10461-010-9803-z
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发表时间:
2010-12
期刊:
影响因子:
4.4
通讯作者:
Fishbein, Martin
Fishbein, Martin
中科院分区:
医学2区
文献类型:
--
作者:
Johnson, Blair T.;Redding, Colleen A.;DiClemente, Ralph J.;Mustanski, Brian S.;Dodge, Brian;Sheeran, Paschal;Warren, Michelle R.;Zimmerman, Rick S.;Fisher, William A.;Conner, Mark T.;Carey, Michael P.;Fisher, Jeffrey D.;Stall, Ronald D.;Fishbein, Martin

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艾滋病毒是通过不同规模的临时或永久网络中连接的个人的二元交换传播的。为了优化预防效果,将关键个人层面要素与重要网络要素联系起来的补充理论视角可以成为制定和实施艾滋病毒干预措施的基础,其结果随着时间的推移更可持续,并具有更大的传播潜力。为此,我们引入了用于艾滋病毒预防的网络-个人-资源(NIR)模型,该模型认识到个人及其网络之间的资源交换如何构成和维持艾滋病毒风险行为。那么,预防艾滋病毒的个人行为改变可能取决于个人相关网络对这种改变的支持程度的增加。除其他影响外,NIR 模型预测预防工作的成功取决于预防工作是否 (1) 促进个人或其网络拥有的资源可以维持的行为改变; (2)满足个人和网络需求,符合个人现状/发展阶段; (3) 受到信任和重视; (4) 针对艾滋病毒高流行网络。
HIV is transmitted through dyadic exchanges of individuals linked in transitory or permanent networks of varying sizes. To optimize prevention efficacy, a complementary theoretical perspective that bridges key individual level elements with important network elements can be a foundation for developing and implementing HIV interventions with outcomes that are more sustainable over time and have greater dissemination potential. Toward that end, we introduce a Network-Individual-Resource (NIR) model for HIV prevention that recognizes how exchanges of resources between individuals and their networks underlies and sustains HIV-risk behaviors. Individual behavior change for HIV prevention, then, may be dependent on increasing the supportiveness of that individual's relevant networks for such change. Among other implications, an NIR model predicts that the success of prevention efforts depends on whether the prevention efforts (1) prompt behavior changes that can be sustained by the resources the individual or their networks possess; (2) meet individual and network needs and are consistent with the individual's current situation/developmental stage; (3) are trusted and valued; and (4) target high HIV-prevalence networks.
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