社会经济地位
健康信息全国趋势调查
心理干预
信息搜寻
可靠性
优势比
置信区间
逻辑回归
可能性
信息寻求行为
横断面研究
多元分析
人口学
家庭收入
描述性统计
学历
心理学
环境卫生
医学
健康信息
人口
地理
医疗保健
统计
经济
护理部
政治学
数学
图书馆学
内科学
计算机科学
社会学
病理
考古
经济增长
法学
作者
Naleef Fareed,Pallavi Jonnalagadda,Christine M. Swoboda,Pranav Samineni,Tyler Griesenbrock,Timothy R. Huerta
标识
DOI:10.1177/08901171211018135
摘要
Purpose: Assessed socioeconomic factors in health information seeking behavior and trust of information sources from 2007 to 2017. Design: Pooled cross-sectional survey data. Setting: Health Information National Trends Survey. Participation: Data included 6 iterations of U.S. adults (Pooled: N = 19,496; 2007: N = 3,593; 2011: N = 3,959; 2013: N = 3,185; FDA 2015: N = 3,738; 2017: N = 3,285; and FDA 2017: N = 1,736). Measures: Outcome variables were health information seeking, high confidence, and high trust of health information from several sources. Independent variables were education and income group, controlling for other sociodemographic variables. Analysis: Weighted descriptive and multivariate logistic regression for the pooled sample assessed associations by education and income. Fully interacted models with education/income-survey year interactions compared differences in outcomes between years. Results: We found information seeking, confidence, and trust were associated with income and education, which supported previously reported findings. Additionally, our findings indicated low-and medium-income groups had significantly lower odds of seeking health information compared to those in a high-income group. Regarding trust of information, a high school education was associated with higher odds of trust in family and friends. We also found that, over time, information seeking, confidence, and trust behavior differed by income and education, with some differences persisting. Conclusion: Disparities by income and education in trust of information sources remained across time. Understanding optimal information sources, their reach, and their credibility among groups could enable more targeted interventions and health messaging. We also describe the implications for our findings in the context of COVID-19.
科研通智能强力驱动
Strongly Powered by AbleSci AI