PREDICTORS OF BREAST CANCER DETECTION BEHAVIORS

为了帮助早期发现乳腺癌，女性应该进行 每月乳房自我检查，定期获得专业乳房 考试 这项拟议中的研究将试图确定为什么女性在 大底特律地区是否进行这些乳腺癌检测 行为。 研究将以理论为指导，公开研究 健康和社会心理学，并将侧重于妇女的信仰， 知识以及年龄、种族和社会经济等人口统计变量 status. 具体目标是：（a）确定信仰、知识和 作为乳腺癌检测重要预测因素的其他变量 行为（B），以确定这些信念和知识的水平， （c）在一般人群和目标亚群中， 妇女使用和信任的健康信息来源，以及 (d)验证与乳房相关的健康信念的改进措施 癌症检测行为。 根据研究结果， 将提出健康教育干预建议， 鼓励乳腺癌检测行为的表现。 具体地说， 将提出建议，说明应强调哪些信息， 在对公众的干预中使用何种沟通渠道 公共和几个目标人群。 数据将在 个人访谈的概率样本的700名成年妇女在 大底特律地区 乳腺癌检测行为将是 在一年的电话随访中重新评估。 大多数预测变量 将通过多项目量表进行测量，这些量表将在 试点工作 多元回归分析将用于确定 当其他变量保持不变时，变量有助于预测。

To facilitate early detection of breast cancer, women should perform breast self-examination monthly and obtain regular professional breast examinations. The proposed research will attempt to determine why women in the greater Detroit area do or do not perform these breast cancer detection behaviors. The research will be guided by theory and research in public health and social psychology and will focus on women's beliefs and knowledge and on the demographic variables of age, race, and socioeconomic status. The specific aims are (a) to identify the beliefs, knowledge, and other variables that are important predictors of breast cancer detection behaviors (b) to determine the level of those beliefs and knowledge in the general population and in target subpopulations, (c) to identify the sources of information about health that are used and trusted by women, and (d) to validate improved measures of health beliefs relevant to breast cancer detection behaviors. On the basis of the research findings, recommendations will be made for health education interventions to encourage performance of breast cancer detection behaviors. Specifically, recommendations will be made about what information to emphasize and about what channels of communication to use in interventions for the general public and for several target populations. Data will be collected in personal interviews with a probability sample of 700 adult women in the greater Detroit area. The breast cancer detection behaviors will be reassessed in a one year follow-up by telephone. Most predictor variables will be measured by multi-item scales which will have been pretested in pilot work. Multiple regression analysis will be used to determine which variables contribute to prediction when other variables are held constant.