To compare self-reported patterns of health service utilisation among residents of urban and rural South Australia. DESIGN, SETTING AND MAIN OUTCOME MEASURES: Secondary analysis of data generated by computer-assisted telephone interviews of 7377 adults done in 1995-6. Respondents were asked if they had used each of 18 different health services during the previous 12 months. Residence was classified in three ways: (1) capital city versus rest of the state, (2) by the Rural, Remote and Metropolitan Areas classification (RRMA) and (3) by the Accessibility and Remoteness Index for Australia classification (ARIA).
General practitioner services were most frequently used, by approximately 89% of respondents. Only 4% reported not using any service. Comparing capital city with rest of the state, modest but statistically significant differences in utilisation (P < 0.01) were measured for nine services. In eight of these nine, utilisation was higher among rural residents. Analysing by RRMA, eight services were reportedly used differently and seven of these were the same as those identified from the capital city versus rest of state comparison. Across the five ARIA categories, six previously identified services were reported as being used differentially. Overall, rural residents had a higher than expected rate of moderate and high level of health service use.
Self-reported use of a range of health services was broadly similar across urban and rural South Australia, with most cases of higher use were reported from rural areas rather than urban areas. Similar results were obtained when residence was classified in the three different ways.
"In attempting to explain urban–rural differences in health, much of the research has emphasized the health delivery challenges of rural areas (Bolin and Gamm, 2003; Campbell et al., 2008; Hanlon et al., 2007), including limited service provision (Hanlon and Halseth, 2005; Kenny and Duckett, 2004; Panelli et al., 2006), lack of physicians (Eberhardt et al., 2001), remoteness from and difficulty traveling to urban health services (Comer and Mueller, 1995), and inadequate health promoting infrastructure (Conradson, 2005; Iredale et al., 2005; Kearns et al., 2006; McCann et al., 2005; Watkins and Jacoby, 2007). There is evidence that many of these disparities are related to the 'degree of rurality' of the geographic areas in question (Dempsey et al., 2003; Eberhardt et al., 2001; Farmer et al., 2010; Fylkesnes et al., 1992; Greiner et al., 2004; Laditka et al., 2009; Larson and Correa-de- Araujo, 2006; Nummela et al., 2008; Prince, 1988; Satcher, 2001). However, much of this research has been conducted in countries outside of the US, has examined only one state or geographic region in the US, or has failed to simultaneously examine individual and contextual explanations for differential health along the continuum of rurality. "
[Show abstract][Hide abstract] ABSTRACT: This research explored the roles of 'rurality' - nonmetropolitan county population size and adjacency to metropolitan areas - on self-rated health among a nationally representative sample of US adults. Using seven years of pooled individual level data from the Behavioral Risk Factor Surveillance System and county-level data from the County Characteristics survey, we found that residents of remote rural counties have the greatest odds of reporting bad health and that the significant differences in self-rated health between metropolitan residents and residents of rural areas can be entirely explained by rural structural disadvantage, including higher rates of unemployment and population loss and lower levels of educational attainment.
Health & Place 11/2010; 17(1). DOI:10.1016/j.healthplace.2010.11.008 · 2.81 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: There is abundant evidence that rural origin is an influence on rural career choice. Rural origin is widely used to select students to be supported into programs designed to address the rural medical workforce shortage. What is not as clear is how many years of rural upbringing are required to have a maximal effect on rural career choice. Neither is the place of having a sense of rural background well understood.
A cross-sectional self-completed paper-based survey of all students in years one through four of the Monash University medical course was undertaken in 2003. The survey included a scale to measure stated rural career intention as well as questions about the number of years of rural upbringing and whether students had a sense of rural background. The Rural Intention score was divided into three categories: strong urban intent, strong rural intent, and an intermediate, less certain intent.
There was an 88% (n = 399) response rate from students holding Commonwealth Supported Places. Approximately 30% of these claimed a sense of rural background, and 28% had more than 8 years of rural upbringing. Twenty-five percent stated a strong intention to choose a rural career and 34.5% had strong urban intent. The remaining 40.5% were in the intermediate group. Almost all students (97.5%) with over 5 years of rural upbringing had developed a sense of rural background, and almost all (97.5%) with less than 5 years' rural upbringing denied a sense of rural background. Rural intent was high for those with a sense of rural background and those with more than 8 years of rural upbringing, but the students who had had from 4 to 8 years of rural upbringing mainly fell into the 'uncertain' category.
In this cohort of almost 400 Australian medical students, a sense of rural background developed at a clear point, around 5 years of rural upbringing. Students with a sense of rural background were likely to develop a strong rural intent several years before similar students who had failed to make this connection with a rural community. This latter group displayed uncertainty toward a rural career choice, possibly due to unfamiliarity. Unlike those with strong urban intent, these students have not excluded a rural career and should be supported. The inclusion of a measure of the intention of students to work in a rural environment is likely to increase the reliability and validity of selection procedures.
Rural and remote health 01/2007; 7(2):706. · 0.88 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: In this paper, a coplanar-waveguide on the mother board connected
by a bump with another coplanar-waveguide on the chip is analyzed in the
range of frequencies between 1 GHz and 30 GHz. Various package-schemes
of a flip-chip are presented and analyzed. The variations include the
length of the overlap of the cpw on the mother-board and the cpw on the
chip. It is concluded that the overlap of the coplanar waveguides is one
of the most important parameters which determine the characteristics of
the bump-connection. Based on the S-parameters, the important physical
parameters of the bump-connections are analyzed and recommendations are
made concerning the bump connection. The S-parameters are used in order
to calculate the Y-parameters, from which the circuit model is obtained.
The parameters of the circuit model are calculated and demonstrated for
frequencies between 1 GHz and 30 GHz. The results presented in this
paper are obtained by a method of moment
Electrical and Computer Engineering, 2000 Canadian Conference on; 02/2000
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