This study investigates college students’ exposure to messages about campus dating and sexual violence (DSV) to discover classes of students based on the message source. Latent class analysis was performed with data assessing 5,284 students’ exposure to 16 different sources of information regarding DSV, from having a professor discuss these topics to participating in related campus events. Several variables were included in the analysis to understand students’ class membership in relation to pertinent student demographic factors. The best model indicated that there are five classes of students based on their exposure to messages, with some subgroups of students having high exposure to various sources of messages about DSV while others have low exposure. Still other subgroups are primarily exposed to messages through nonactive or noninstitutional sources of information. Subgroups of students, based on their exposure to DSV messaging, also differ regarding demographic makeup. The implications from these results include the need to tailor DSV programming on campus to subgroups of students based on their varying baseline levels of message exposure. As students are exposed to messaging through differing sources, including passive and noninstitutional, institutions can use this information when designing outreach efforts to better fit with the needs of various subgroups of students.

本研究调查大学生对校园约会和性暴力 (DSV) 消息的接触情况，以根据消息源发现学生班级。使用数据进行潜在班级分析，评估 5,284 名学生接触 16 种不同的 DSV 信息来源，从让教授讨论这些主题到参与相关的校园活动。分析中包含了几个变量，以了解与相关学生人口因素相关的学生班级成员资格。最佳模型表明，根据学生对消息的接触程度，有五类学生，其中一些学生子组对有关 DSV 的各种消息来源的接触率较高，而另一些学生的接触率较低。还有一些子群体主要通过非活动或非机构的信息来源接触到信息。根据对 DSV 消息的接触程度，学生子群体在人口构成方面也存在差异。这些结果的影响包括需要根据学生不同的信息暴露基线水平，为学生群体定制校园内的 DSV 编程。由于学生通过不同的来源（包括被动和非机构）接触到信息，因此机构可以在设计外展工作时使用这些信息，以更好地满足不同学生群体的需求。这些结果的影响包括需要根据学生不同的信息暴露基线水平，为学生群体定制校园内的 DSV 编程。由于学生通过不同的来源（包括被动和非机构）接触到信息，因此机构可以在设计外展工作时使用这些信息，以更好地满足不同学生群体的需求。这些结果的影响包括需要根据学生不同的信息暴露基线水平，为学生群体定制校园内的 DSV 编程。由于学生通过不同的来源（包括被动和非机构）接触到信息，因此机构可以在设计外展工作时使用这些信息，以更好地满足不同学生群体的需求。