Yair Zalmanovitch1, Dana R Vashdi1. 1. Division of Public Administration and Policy, School of political Sciences, University of Haifa, Haifa, Israel.
Abstract
OBJECTIVE: To examine whether there are individual level factors such as socio-economic status that may predict disparities in the public's experiences with and expectations of their health-care providers. DATA SOURCES/STUDY SETTING: Primary data were collected in 2010. The sample comprised of 1211 Israeli citizens above the age of 18. STUDY DESIGN: Participants were randomly approached at one points in time and presented with statements regarding practices they experience and practices that are important to them related to primary care, preventive care and health promotion. We calculated a difference scores for each health-care area. We measured socio-economic status (SES) with three separate variables relating to income, education and living location. DATA COLLECTION/EXTRACTION METHODS: Employees of a professional telephone survey firm conducted the survey. Multiple regression was used with the responsiveness gap in each of three health-care areas as the dependent variables. PRINCIPAL FINDINGS: We found that level of education is negatively related to the extent of the responsiveness gap in both primary and preventive health care and that income is negatively related to the responsiveness gap in health promotion. CONCLUSIONS: Personal characteristics such as SES are related to people's perceptions about the extent of the responsiveness gap. Policy makers can now expend efforts and resources in minimizing such responsiveness gaps among specific populations.
OBJECTIVE: To examine whether there are individual level factors such as socio-economic status that may predict disparities in the public's experiences with and expectations of their health-care providers. DATA SOURCES/STUDY SETTING: Primary data were collected in 2010. The sample comprised of 1211 Israeli citizens above the age of 18. STUDY DESIGN:Participants were randomly approached at one points in time and presented with statements regarding practices they experience and practices that are important to them related to primary care, preventive care and health promotion. We calculated a difference scores for each health-care area. We measured socio-economic status (SES) with three separate variables relating to income, education and living location. DATA COLLECTION/EXTRACTION METHODS: Employees of a professional telephone survey firm conducted the survey. Multiple regression was used with the responsiveness gap in each of three health-care areas as the dependent variables. PRINCIPAL FINDINGS: We found that level of education is negatively related to the extent of the responsiveness gap in both primary and preventive health care and that income is negatively related to the responsiveness gap in health promotion. CONCLUSIONS: Personal characteristics such as SES are related to people's perceptions about the extent of the responsiveness gap. Policy makers can now expend efforts and resources in minimizing such responsiveness gaps among specific populations.
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