GENDER, ETHNICITY, AND MENTAL HEALTH IN PRIMARY CARE

简介(改编自申请者摘要)：建议项目为 专注于心理健康研究中被忽视的领域--患者 性别、种族和年龄影响精神疾病的讨论和治疗 初级保健就诊期间的健康问题。这是一个重要的 研究领域，因为许多患者只看初级保健医生，而不是 心理健康专家，为他们的心理社会关切(Greenley等人)。 1987年；Leaf等人，1988年)。为了更好地了解患者的耐心 性别、种族和年龄影响心理社会问题是如何被 在初级保健环境中讨论和治疗，现有的数据集将 使用。数据集包括427名门诊患者的录音和成绩单 访问以及访问后对患者和医生的访谈。这个 数据集最初是在1995年期间在普通医学和 新墨西哥大学健康科学分校家庭诊所 中心。该数据集非常独特，因为它是仅有的 收集的医患互动录音带收集于 包括说西班牙语和说英语的人的美国。这个 将开发以下编码工具：(1)患者表情 情绪症状和对家庭、社交或个人生活的讨论(2) 患者的身体症状表现，以及(3)医生和患者 抗抑郁药和小镇静剂的讨论。427辆中的每一辆 医生和患者之间的互动将使用开发的 乐器。然后，编码的医生-患者交互数据将 与医疗和药房记录数据以及患者和 医生访谈数据。分层建模和随机效果 将使用回归技术来分析数据。

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.