School of Public Health, Bielefeld University, 33615 Bielefeld, Germany.
Department of Health and Nursing Sciences, Faculty of Social and Health Sciences, Inland Norway University of Applied Sciences, 2418 Elverum, Norway.
Int J Environ Res Public Health. 2022 Oct 25;19(21):13863. doi: 10.3390/ijerph192113863.
To manoeuvre a complex and fragmented health care system, people need sufficient navigational health literacy (NAV-HL). The objective of this study was to validate the HLS-NAV measurement scale applied in the European Health Literacy Population Survey 2019-2021 (HLS). From December 2019 to January 2021, data on NAV-HL was collected in eight European countries. The HLS-NAV was translated into seven languages and successfully applied in and validated for eight countries, where language and survey method differed. The psychometric properties of the scale were assessed using confirmatory factor analysis (CFA) and Rasch modelling. The tested CFA models sufficiently well described the observed correlation structures. In most countries, the NAV-HL data displayed acceptable fit to the unidimensional Rasch partial credit model (PCM). For some countries, some items showed poor data-model fit when tested against the PCM, and some items displayed differential item functioning for selected person factors. The HLS-NAV demonstrated high internal consistency. To ensure content validity, the HLS-NAV was developed based on a conceptual framework. As an estimate of discriminant validity, the Pearson correlations between the NAV-HL and general health literacy (GEN-HL) scales were computed. Concurrent predictive validity was estimated by testing whether the HLS-NAV, like general HL measures, follows a social gradient and whether it forms a predictor of general health status as a health-related outcome of general HL. In some countries, adjustments at the item level may be beneficial.
为了驾驭复杂且碎片化的医疗体系,人们需要具备足够的导航健康素养(NAV-HL)。本研究旨在验证欧洲健康素养人群调查 2019-2021 年(HLS)中应用的 HLS-NAV 测量量表。2019 年 12 月至 2021 年 1 月,在 8 个欧洲国家收集了关于 NAV-HL 的数据。HLS-NAV 被译为 7 种语言,并成功应用于 8 个国家,这些国家的语言和调查方法存在差异。使用验证性因子分析(CFA)和 Rasch 模型评估了该量表的心理测量学特性。经检验,CFA 模型充分描述了观测到的相关结构。在大多数国家,NAV-HL 数据与单维 Rasch 部分信用模型(PCM)具有可接受的拟合度。在某些国家,一些项目在与 PCM 进行测试时显示出较差的数据-模型拟合,而一些项目在针对特定个体因素时显示出项目功能差异。HLS-NAV 具有较高的内部一致性。为确保内容效度,HLS-NAV 是基于概念框架开发的。作为区分效度的估计,计算了 NAV-HL 与一般健康素养(GEN-HL)量表之间的 Pearson 相关系数。通过测试 HLS-NAV 是否像一般 HL 测量一样遵循社会梯度,以及它是否作为一般 HL 的健康相关结果形成一般健康状况的预测因子,来评估同时预测效度。在某些国家,项目层面的调整可能是有益的。