Yi-Jing Huang1, Cheng-Te Chen2, Gong-Hong Lin1, Tzu-Yi Wu3, Sheng-Shiung Chen4, Li-Fong Lin5,6, Wen-Hsuan Hou7,8,9, Ching-Lin Hsieh1,10. 1. School of Occupational Therapy, College of Medicine, National Taiwan University, Taipei, Taiwan. 2. Department of Educational Psychology and Counseling, National Tsing Hua University, Hsinchu, Taiwan. 3. Institute of Economics, Academia Sinica, Taipei, Taiwan. 4. Department of Physical Medicine and Rehabilitation, E-Da Hospital/I-Shou University, Kaohsiung, Taiwan. 5. Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan. 6. School of Gerontology and Health Management, College of Nursing, Taipei Medical University, Taipei, Taiwan. 7. School of Gerontology and Health Management, College of Nursing, Taipei Medical University, Taipei, Taiwan. houwh@tmu.edu.tw. 8. Master Program in Long-Term Care, College of Nursing, Taipei Medical University, No. 250, Wuxing St., Xinyi Dist., Taipei, 110, Taiwan. houwh@tmu.edu.tw. 9. Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei, Taiwan. houwh@tmu.edu.tw. 10. Department of Physical Medicine and Rehabilitation, National Taiwan University Hospital, Taipei, Taiwan.
Abstract
BACKGROUND: The effective self-management and treatment of long-term disability after stroke depends greatly on the health literacy of patients. The European Health Literacy Survey Questionnaire (HLS-EU-Q) is a comprehensive and theory-based measure that captures multiple self-perceived competencies of health literacy and covers a diverse range of health contexts. However, there is no psychometric evidence on the HLS-EU-Q in the stroke population. OBJECTIVE: The aim of this study was to examine the validity of the HLS-EU-Q in patients with stroke using Rasch analysis. METHODS: We compared the model deviance among the one-domain, three-domain, four-domain, and 12-domain structures using likelihood ratio tests to determine the dimensionality of the HLS-EU-Q. Thereafter, we examined the unidimensionality of each domain, local independence, item fit, response categories, and differential item functioning (DIF) for the best fitting structure. RESULTS: A total of 311 patients with stroke participated in this study. Rasch analysis revealed that the 12-domain HLS-EU-Q demonstrated the best data-model fit. The original 4-point scales showed disordering, which can be corrected by rescaling them as 3-point scales with the two middle categories collapsed. All 47 items in the rescaled HLS-EU-Q fit the 12-domain Rasch model, demonstrated local independence, assessed the 12 unidimensional domains respectively, and had invariant difficulties between different age or education groups of the patients with stroke. CONCLUSION: We recommend using the 12-domain scores of the rescaled HLS-EU-Q to comprehensively and accurately capture the competencies to access, understand, appraise, and apply health information within the three health contexts of healthcare, disease prevention, and health promotion for patients with stroke.
BACKGROUND: The effective self-management and treatment of long-term disability after stroke depends greatly on the health literacy of patients. The European Health Literacy Survey Questionnaire (HLS-EU-Q) is a comprehensive and theory-based measure that captures multiple self-perceived competencies of health literacy and covers a diverse range of health contexts. However, there is no psychometric evidence on the HLS-EU-Q in the stroke population. OBJECTIVE: The aim of this study was to examine the validity of the HLS-EU-Q in patients with stroke using Rasch analysis. METHODS: We compared the model deviance among the one-domain, three-domain, four-domain, and 12-domain structures using likelihood ratio tests to determine the dimensionality of the HLS-EU-Q. Thereafter, we examined the unidimensionality of each domain, local independence, item fit, response categories, and differential item functioning (DIF) for the best fitting structure. RESULTS: A total of 311 patients with stroke participated in this study. Rasch analysis revealed that the 12-domain HLS-EU-Q demonstrated the best data-model fit. The original 4-point scales showed disordering, which can be corrected by rescaling them as 3-point scales with the two middle categories collapsed. All 47 items in the rescaled HLS-EU-Q fit the 12-domain Rasch model, demonstrated local independence, assessed the 12 unidimensional domains respectively, and had invariant difficulties between different age or education groups of the patients with stroke. CONCLUSION: We recommend using the 12-domain scores of the rescaled HLS-EU-Q to comprehensively and accurately capture the competencies to access, understand, appraise, and apply health information within the three health contexts of healthcare, disease prevention, and health promotion for patients with stroke.
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