There is an expectation that health professions schools respond to priority societal health needs. This expectation is largely based on the underlying assumption that schools are aware of the priority needs in their communities. This paper demonstrates how open-access, pan-national health data can be used to create a reliable health index to assist schools in identifying societal needs and advance social accountability in health professions education. Using open-access data, a psychometric evaluation was conducted to examine the reliability and validity of the Canadian Health Indicators Framework (CHIF) conceptual model. A non-linear confirmatory factor analysis (CFA) on 67 health indicators, at the health-region level (n = 97) was used to assess the model fit of the hypothesized 10-factor model. Reliability analysis using McDonald's Omega were conducted, followed by Pearson's correlation coefficients. Findings from the non-linear CFA rejected the original conceptual model structure of the CHIF. Exploratory post hoc analyses were conducted using modification indices and parameter constraints to improve model fit. A final 5-factor multidimensional model demonstrated superior fit, reducing the number of indicators from 67 to 32. The 5-factors included: Health Conditions (8-indicators); Health Functions (6-indicators); Deaths (5-indicators); Non-Medical Health Determinants (7-indicators); and Community & Health System Characteristics (6-indicators). All factor loadings were statistically significant (p < 0.001) and demonstrated excellent internal consistency ([Formula: see text] >0.95). Many schools struggle to identify and measure socially accountable outcomes. The process highlighted in this paper and the indices developed serve as starting points to allow schools to leverage open-access data as an initial step in identifying societal needs.

中文翻译：

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卫生专业学校社会责任健康指数的有效性证据和心理测量评估。

人们期望卫生专业学校能够满足优先的社会卫生需求。这种期望很大程度上基于这样的基本假设：学校了解其社区的优先需求。本文展示了如何使用开放获取的泛国家健康数据来创建可靠的健康指数，以帮助学校确定社会需求并推进卫生专业教育中的社会责任。使用开放数据进行心理测量评估，以检验加拿大健康指标框架（CHIF）概念模型的可靠性和有效性。在健康区域水平 (n = 97) 对 67 项健康指标进行非线性验证性因素分析 (CFA)，用于评估假设的 10 因素模型的模型拟合度。使用McDonald’s Omega 进行可靠性分析，然后进行皮尔逊相关系数分析。非线性 CFA 的结果否定了 CHIF 最初的概念模型结构。使用修改指数和参数约束进行探索性事后分析以改进模型拟合。最终的 5 因素多维模型表现出卓越的拟合度，将指标数量从 67 个减少到 32 个。这 5 个因素包括： 健康状况（8 个指标）；健康功能（6项指标）；死亡人数（5 个指标）；非医疗健康决定因素（7 个指标）；以及社区和卫生系统特征（6 个指标）。所有因子载荷均具有统计显着性（p < 0.001），并表现出出色的内部一致性（[公式：参见文本] > 0.95）。许多学校都在努力识别和衡量对社会负责的成果。本文强调的过程和制定的指数是学校利用开放获取数据作为确定社会需求的第一步的起点。