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A Self-Disclosure Model for Personal Health Information

机译:个人健康信息的自披露模型

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The use of information technologies (IT) to collect personal health information is growing in popularity via computer-assisted interviewing and a wide variety of healthcare Web sites. However, a review of the literature on computer-assisted interviewing exhibits confounding and equivocal results regarding the effects of IT on individuals' willingness to disclose socially sensitive health information. Some studies revealed individuals' heightened concerns about entering their health information into a computer, while other studies exhibited greater willingness to enter sensitive information into a computer than to give it to a personal physician. The pervading lack of clarity in explaining these results may be largely due to limited attempts to model the underlying factors that motivate the self-disclosure of socially sensitive personal health information; most studies examine the relationship between the data collection environment and the willingness to self disclose without identifying the underlying factors. In this paper, we propose a model of self-disclosure that contains three classes of motivating factors derived from a decomposition of the data collection environment of previous studies: perceived privacy, context sensitivity, and quality of feedback. Aspects of the data collection environment that reinforce the motivational factors are expected to increase disclosure and thus improve the quality of information. An analysis of the results of previous studies employing IT-enabled data-collection environments offers preliminary support of the proposed model. After presenting the model, we discuss research implications and suggest approaches for validating the self-disclosure model.
机译:利用信息技术(IT)来收集个人健康信息的普及越来越多通过计算机辅助面试和各种各样的医疗网站。然而,在计算机辅助访谈展品混淆和关于IT的个人意愿的影响模棱两可的结果文献的回顾披露社会敏感的健康信息。一些研究表明个人有关输入自己的健康信息到计算机的极大关注,而另一些研究显示出更大的意愿输入敏感信息到计算机,而不是把它给一个私人医生。弥漫缺乏清晰度在解释这些结果可能是主要是由于尝试该激励的社会敏感个人健康信息的自公开的潜在因素建模限制;大多数研究检查数据采集环境和意愿之间的关系,以自我披露不识别的潜在因素。在本文中,我们提出了一个包含三类激励既往研究的数据采集环境的分解得到的因素的自我公开的模式:感知隐私,语境敏感性和反馈质量。即强化激励因素的数据收集环境的各方面预期增加披露,从而提高信息的质量。采用前人的研究结果的分析基于IT的数据收集环境提供了模型的初步支持。呈现模型后,我们讨论研究的意义,并提出了方法验证自我表露模型。

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