Understanding and addressing populations whose prior experience has led to mistrust in healthcare

Israel journal of health policy research(2023)

引用 1|浏览0
暂无评分
摘要
Background Policy makers need to maintain public trust in healthcare systems in order to foster citizen engagement in recommended behaviors and treatments. The importance of such commitment has been highlighted by the recent COVID-19 pandemic. Central to public trust is the extent of the accountability of health authorities held responsible for long-term effects of past treatments. This paper addresses the topic of manifestations of trust among patients damaged by radiation treatments for ringworm. Methods For this mixed-methods case study (quan/qual), we sampled 600 files of Israeli patients submitting claims to the National Center for Compensation of Scalp Ringworm Victims in the years 1995–2014, following damage from radiation treatments received between 1946 and 1960 in Israel and/or abroad. Qualitative data were analyzed with descriptive statistics, and correlations were analyzed with chi-square tests. Verbal data were analyzed by the use of systematic content analysis. Results Among 527 patients whose files were included in the final analysis, 42% held authorities responsible. Assigning responsibility to authorities was more prevalent among claimants born in Israel than among those born and treated abroad ( χ 2 = 6.613, df = 1, p = 0.01), claimants reporting trauma ( χ 2 = 4.864, df = 1, p = 0.027), and claimants living in central cities compared with those in suburban areas ( χ 2 = 18.859, df = 6, p < 0.01). Men, younger claimants, patients with a psychiatric diagnosis, and patients from minority populations expressed mistrust in health regulators. Conclusions Examining populations' perceived trust in healthcare institutions and tailoring health messages to vulnerable populations can promote public trust in healthcare systems.
更多
查看译文
关键词
Public trust,Accountability,Clarity of responsibility/ liability,Health messaging,Disclosure,Radiation treatments,Reparations,Population health
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要