Mental health is a complex, multidimensional concept that goes beyond clinical diagnoses, including psychological distress, life stress and well-being. This study aims to use unsupervised clustering approaches to identify multidimensional mental health profiles that exist in the population, and their associated service use patterns. The data source for this study is the 2012 Canadian Community Health Survey- Mental Health linked to administrative healthcare data holdings, included were all Ontario adult respondents. We used a Partioning Around Medoids clustering algorithm with Gower’s proximity to identify groups with distinct combinations of mental health indicators and described them by their sociodemographic and service use characteristics. We identified four groups with distinct mental health profiles, including one group who met the clinical threshold for a depressive diagnosis, with the remaining three groups expressing differences in positive mental health, life stress and self-rated mental health. The four groups had different age, employment and income profiles and exhibited differential access to mental healthcare services. This study represents the first step in identifying complex profiles of mental health at the population level in Ontario, Canada. Further research is required to better understand the potential causes and consequences of belonging to each of the mental health profiles identified.

中文翻译：

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使用无监督聚类方法来识别加拿大安大略省的常见心理健康状况和相关的心理保健服务使用模式

心理健康是一个复杂的、多维的概念，超越了临床诊断，包括心理困扰、生活压力和幸福感。本研究旨在使用无监督聚类方法来识别人群中存在的多维心理健康状况及其相关的服务使用模式。本研究的数据来源是 2012 年加拿大社区健康调查 - 与行政医疗保健数据相关的心理健康，其中包括所有安大略省成年受访者。我们使用围绕 Medoids 的分区聚类算法和高尔邻近度来识别具有不同心理健康指标组合的群体，并通过其社会人口统计和服务使用特征来描述它们。我们确定了四组具有不同心理健康状况的群体，其中一组满足抑郁诊断的临床阈值，其余三组在积极心理健康、生活压力和自评心理健康方面表现出差异。这四个群体有不同的年龄、就业和收入状况，并且在获得心理保健服务方面表现出差异。这项研究代表了确定加拿大安大略省人群心理健康复杂状况的第一步。需要进一步的研究来更好地了解属于每种已确定的心理健康状况的潜在原因和后果。