健康消费者、社会经济因素和健康状况对放射学健康素养的预测作用

Mohammad Alarifi, Timothy Patrick, A. Jabour, Min Wu, Jake Luo
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引用次数: 0

摘要

患者对放射学的了解是患者参与护理和管理自己健康的必要条件。很少知道的因素,可以预测病人的识字放射学报告,测试,或治疗。本研究旨在确定健康消费、社会经济和健康状况的最重要因素,作为放射学领域健康素养的预测因子。这项研究招募了616名参与者,使用亚马逊的土耳其机器人(MTURK),并向这些参与者发放了我们的问卷。我们测量了参与者的放射学认知水平、社会因素和健康状况。描述性统计包括卡方和线性回归模型来检验这些因素是否可以预测放射学素养。计算接受者-操作者曲线下面积,确定回归模型的预测精度。线性回归显示,19个社会经济因素和健康状况中有15个因素与放射学素养显著相关(P < 0.05)。另一方面,使用皮尔逊卡方分析,19个因素中只有12个具有显著性(P < 0.05)。逐步线性回归分析表明,12个公因式因子中有9个的r平方呈线性关系。这些因素是教育水平、吸烟、放射经验、保险状况、白人种族、就业状况、残疾状况、性别和收入(0.209)。这9个因素具有较好的预测放射学素养的能力(受试者操作曲线下面积为0.677 [95%CI为0.549;0.804, p = 0.013])。社会经济因素和健康状况可以成功地预测放射学素养。通过比较社会因素、健康状况与放射学意识,我们能够成功地识别出与放射学素养高度相关的预测因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Health Consumer Social Economic Factors and Health Conditions as Predictor for Health Literacy in Radiology Domain
Patient literacy of radiology is imperative for patient engagement in care and management of their own health. Little is known about the factors that could predict patient literacy of radiology reports, testing, or treatment. This study aims to identify the most important factors of health consumer social economic and health conditions as a predictor of health literacy in the radiology domain. The study recruited 616 participants using Amazon.com’s Mechanical Turk (MTURK) and presented these participants with our questionnaire. We measured the level of participants’ radiology awareness, social factors, and health status. Descriptive statics including Chi-Square and linear regression models were used to test if the factors could predict radiology literacy. The area under the receiver–operator curve was calculated to determine the prediction accuracy of the regression models. linear regression indicated that 15 of the 19 social-economic factors and health conditions were significantly associated with radiology literacy (P < .05). On the other hand, only 12 of the 19 factors were significant by using Pearson Chi-Square (P < .05). Stepwise linear regression analysis demonstrated the r squared linear of 9 out of 12 common factors. These factors are the level of education, smoking, radiology experience, insurance status, white race, employment status, disability status, gender, and income at 0.209. These nine factors had a good ability to predict radiology literacy (area under the receiver operator curve of 0.677 [95%CI 0.549; 0.804, P = 0.013]). Social economic factors and health conditions can be used to successfully predict radiology literacy. We were able to successfully identify the predictive factors that have a high association with the radiology literacy by comparing social factors and health status versus radiology awareness.
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