GENDER, ETHNICITY, AND MENTAL HEALTH IN PRIMARY CARE

描述（改编自申请人摘要）：拟议项目是 专注于心理健康研究中被忽视的领域-患者如何 性别、种族和年龄影响着对精神疾病的讨论和治疗。 初级保健医疗访问期间的健康问题。 这是一个重要 研究领域，因为许多患者只看到初级保健医生，而不是 心理健康专家，因为他们的心理社会问题（Greenley et al. 1987; Leaf等人，1988年）。 为了更好地了解病人 性别、种族和年龄影响心理社会问题如何被 在初级保健环境中讨论和处理，现有的数据集将 采用 数据集包括427名门诊病人的录音带和文字记录 访问以及访问后与患者和医生的访谈。 的 数据集最初是1995年在普通医学中心收集的， 新墨西哥州健康科学大学家庭诊所 中心 该数据集是非常独特的，因为它是唯一的 收集的医患互动录音带， 包括讲西班牙语和英语的人。 的 将开发以下编码工具：（1）患者表达 情绪症状和家庭，社会或个人生活的讨论，（2） 患者的身体症状表达，和（3）医生和患者 讨论抗抑郁药和小型镇静剂 427人中的每一个 将使用开发的 仪器. 然后，编码的医生-患者交互数据将被 与医疗和药房记录数据和患者合并， 医生访谈数据。 分层建模和随机效应 将使用回归技术分析数据。

DESCRIPTION (Adapted from applicant's abstract): The proposed project is focused on a neglected area within mental health research - how patient gender, ethnicity, and age influence the discussion and treatment of mental health problems during primary care medical visits. This is an important area of research because many patients see only primary care physicians, not mental health specialists, for their psychosocial concerns (Greenley et al. 1987; Leaf et al., 1988). To gain a better understanding of how patient gender, ethnicity, and age influence how psychosocial issues are being discussed and treated in primary care settings, an existing data set will be used. The data set includes audio-tapes and transcripts of 427 outpatient visits as well as post-visit interviews with patients and physicians. The data set was originally collected during 1995 at the general medicine and family practice clinics of the University of New Mexico Health Sciences Center. The data set is extremely unique in that it is one of the only collections of audio-tapes of physician-patient interactions collected in the United States that includes Spanish as well as English speakers. The following coding tools will be developed: (1) patient expression of emotional symptoms and discussion of family, social, or personal life, (2) patient physical symptom expression, and (3) physician and patient discussion of antidepressants and minor tranquilizers. Each of the 427 physician-patient interactions will be coded using the developed instruments. The coded physician-patient interaction data will then be merged with the medical and pharmacy record data and the patient and physician interview data. Hierarchial modeling and random effects regression techniques will be used to analyze the data.