MEASURING CONTINUITY OF CARE IN MENTAL HEALTH SERVICES

关注心理健康的提供者、决策者和研究人员 服务部门一再提到连续性的重要性， 特别是对患有严重精神障碍的人， 多种和持续的服务需求。然而，研究的连续性， 由于测量不准确，护理工作受到阻碍。因此，目前， 护理的连续性仍然是一个未经检验的假设，而不是一个 有经验证据支持的陈述。拟议项目将 制定一个正式的，定量的措施，在精神疾病的护理连续性 卫生服务使用通过人种学研究产生的数据。的 措施将是一个结构化的采访，旨在与消费者使用， 受访者。该项目分三个阶段进行。首先是 收集民族志数据并分析这些数据， 采访的初稿。第二阶段是试点测试， 对30名严重精神病患者进行了访谈， 紊乱初步的心理测量分析将使用 试点测试数据和必要时对访谈进行的修订。的 第三阶段是实地测试和心理测量分析。四 将对100名患有严重精神障碍的人进行面谈， 用于评估的心理测量特性的所得数据， 采访心理测量分析计划要求检查： (1)数据质量，（2）汇总评分尺度假设，（3）项目- 判别效度和量表成功率;（4）量表的信度 分数，（5）分数分布特征，（6）结构效度。 一些可能影响有效性的偏见来源也将被 考察

Providers, policymakers and researchers concerned with mental health services repeatedly make reference to the importance of continuity of care, particularly for persons with severe mental disorders who have multiple and ongoing service needs. However, research on continuity of care has been impeded by poor measurement. Thus at present the importance of continuity of care remains an untested assumption rather than a statement supported by empirical evidence. The proposed project will develop a formal, quantitative measure of continuity of care in mental health services using data generated through an ethnographic study. The measure will be a structured interview designed for use with consumers as respondents. The project takes place in three stages. The first is the collection of ethnographic data and the analysis of these data to produce the first draft of the interview. The second stage is a pilot test in which the interview is administered to 30 persons with severe mental disorders. Preliminary psychometric analyses will be carried out using the pilot test data and revisions to the interview made as needed. The project's third stage is a field test and psychometric analyses. Four hundred persons with severe mental disorders will be interviewed and the resulting data used in evaluating the psychometric properties of the interview. The plan for psychometric analysis calls for examination of: (1) data quality, (2) summated-rating scaling assumptions, (3) item- discriminant validity and scaling success rates, (4) reliability of scale scores, (5) features of score distributions, and (6) construct validity. A number of sources of bias that might affect validity will also be examined.