Special report : the wellbeing of Australians : groups with the highest and lowest wellbeing in Australia

Overview and Aim
1. This report concerns an analysis of the cumulative data from 15 surveys using the Personal Wellbeing Index to measure Subjective Wellbeing. The total number of respondents is about 30,000 but not all data were available for all analyses due to changing variables between
surveys.
2. The aim of this analysis is to determine those sub-groups with the highest and the lowest wellbeing.
Method
3. The definition of sub-groups is through the demographic variables of Income, Gender, Age, Household Composition, Relationship Status and Employment Status. Index domains are also included. While not every combination of demographic variables has been tested, the total number of combinations analysed was 3,277.
4. Extreme group mean scores are defined as lying above 79 points and below 70 points. These values are at least five standard deviations beyond the total sample mean score and are, therefore, extreme outliers. The minimum number of responses that could form such a group is
one. Data are accumulated across surveys for corresponding groups.
Results
5. The initial search for the most extreme groups identified the 20 highest and the 20 lowest groups with a minimum N=10. These are termed the ‘Exclusive’ groups since they were based only on the previously identified extreme scores. In order to determine the true mean of each of these groups, a further analysis incorporated all respondents who met the definition of group membership. For example, an Exclusive group defined as [male, 76+ years] would contain only the accumulation of scores from individual surveys that met the extreme score criterion (<70 or >79). The Inclusive group included the scores from all survey respondents who matched the group definition of male, 76+ years.
6. The results revealed a dominance by the domains of the Personal Wellbeing Index. The extreme high groups were predicted by high scores on all domains except safety and relationships. The low groups were defined by low scores on all seven domains.
7. A further search for extreme groups was undertaken that was restricted to the demographic descriptors. The 20 highest and 20 lowest groups were identified based on a minimum cell content of N=10. The corresponding Inclusive group means were then calculated as before.
8. In order to increase the reliability of the final groups, a minimum cell content of N=20 cases was imposed.
9. Six extreme high groups were identified. These are dominated by high income and the presence of a partner. Five extreme low groups were identified. These are dominated by very low income, the absence of a partner, and unemployment.
Conclusions
10. The conclusions drawn from these analyses are as follows:
10.1 The central defining characteristics of people forming the extreme high wellbeing groups is high household income and living with a partner.
10.2 The central defining risk factors for people forming the extreme low wellbeing groups are very low household income, not living with a partner, and unemployment.
10.3 None of these five demographic characteristics are sufficient to define extreme wellbeing groups on their own. They all act in combinations of at least two risk factors together.

The early postnatal period : exploring women's views, expectations and experiences of care using focus groups in Victoria, Australia

Background: There is growing evidence from Australia and overseas that the care provided in hospital in the early postnatal period is less than ideal for both women and care providers. Many health services face increasing pressure on hospital beds and have limited physical space available to care for mothers and their babies. We aimed to gain a more in-depth understanding of women's views, expectations and experiences of early postnatal care.

Methods: We conducted focus groups in rural and metropolitan Victoria, Australia in 2006. Fifty-two people participated in eight focus groups and four interviews. Participants included eight pregnant women, of whom seven were pregnant with their first baby; 42 women who were in the postpartum period (some up to twelve months after the birth of their baby); and two partners. All participants were fluent in English. Focus group guides were developed specifically for the study and explored participants' experiences and/or expectations of early postnatal care in hospital and at home, with an emphasis on length of hospital stay, professional and social support, continuity of care, and rest. Discussions were audio-taped and transcribed verbatim. A thematic network was constructed to describe and connect categories with emerging basic, organizing, and global themes.

Results: Global themes that emerged were: anxiety and/or fear; and the transition to motherhood and parenting. The needs of first time mothers were considered to be different to the needs of women who had already experienced motherhood. The women in this study were generally concerned about the safety of their new baby, and lacked confidence in themselves as new mothers regarding their ability to care for their baby. There was a consistent view that the physical presence and availability of professional support helped alleviate these concerns, and this was especially the case for women having a first baby.

Conclusion: Women have anxieties and fears around early parenting and their changing role, and may consider that the physical availability of professional care providers will help during this time. Care providers should be cognisant of these potential issues. It is crucial that women's concerns and needs be considered when service delivery changes are planned. If anxiety around new parenting is a predominant view then care providers need to recognise this and ensure care is individualised to address each woman's/families particular concerns.

Using supervised and unsupervised techniques to determine groups of patients with different doctor-patient stability

Decision trees and self organising feature maps (SOFM) are frequently used to identify groups. This research aims to compare the similarities between any groupings found between supervised (Classification and Regression Trees - CART) and unsupervised classification (SOFM), and to identify insights into factors associated with doctor-patient stability. Although CART and SOFM uses different learning paradigms to produce groupings, both methods came up with many similar groupings. Both techniques showed that self perceived health and age are important indicators of stability. In addition, this study has indicated profiles of patients that are at risk which might be interesting to general practitioners.

Dealing with the challenges of teaching large groups

It is now well established that teaching a large class poses significant challenges in tertiary education (Biggs, 2003; Ramsden 2003). In this era of internationalization of education, the problems of teaching a large class are further compounded by the background and diversity of the student cohort. As the student groups become diverse they create challenges in a range of issues including designing the curriculum to appeal to large groups with varying learning styles, assessment, motivation, and creating a suitable learning environment.
Critical reflecting on one’s experiences is a necessary condition for improving teaching (Ramsden 2003). Brookfield (1995: 27) argues that critical reflection should be done from ‘as many unfamiliar angles as possible’ and proposes the use of four critical reflective lenses which include own experiences as a learner and a teacher, students perspectives, colleagues perspectives and though the lens of literature.
In this presentation, I intend to draw upon my experiences of teaching MMM132, one of the largest units in Deakin, to discuss both the challenges of teaching a large class and addressing some of these challenges. In doing so, I will explain how I used Brookfield’s (1995) critical reflection process by reflecting critically upon my own experiences as a teacher and a learner, using my students’ eyes, my colleagues’ perspectives and drawing on the existing literature to address the challenges of teaching a large group.

Role of age, sex, and obesity in the higher prevalence of arthritis among lower socioeconomic groups: a population-based survey

Objective: To compare the prevalence of arthritis among population groups based on demographic, socioeconomic, and body mass index (BMI) characteristics; to investigate the combined influence of these factors on arthritis; and to assess the relationship between self-reported health and psychological distress and arthritis.

Methods: Data from the Victorian Population Health Survey (n = 7,500) were used in the study. Psychological distress was assessed using the Kessler Psychological Distress scale, and self-reported health was assessed by a single item. Multiple logistic regression was used to investigate the combined influence of demographic and socioeconomic factors and BMI on arthritis.

Results: Overall, 23% of Victorian adults (20% men and 26% women) reported having arthritis. The presence of arthritis was associated with high psychological distress (odds ratio [OR] 1.2; 95% confidence interval [95% CI] 1.1-1.4) and poor self-reported health (OR 1.9; 95% CI 1.7-2.1). Increased prevalence of arthritis was found in older age groups, lower education and income groups, and in people who were overweight or obese. Women had higher risk of arthritis, even after adjustment for age, residence, education, occupation, income, and BMI. Age and BMI independently predicted arthritis for men and women. For men, higher risk of arthritis was also associated with lower income.

Conclusion: Arthritis is a highly prevalent condition associated with poor health and high psychological distress. Prevalence of arthritis is disproportionately high among women and individuals from lower socioeconomic backgrounds. As the prevalence of arthritis is predicted to increase, careful consideration of causal factors, and setting priorities for resource allocation for the treatment and prevention of arthritis are required.

Effective farmer groups for defining best practices for sustainable agriculture

Practitioners and farmers are practical people. They are likely to be more comfortable with a process that develops monitoring tools and benchmarks for natural resource management than a process of group development and social capital formation. Yet the two are intrinsically linked. Policy makers and extension workers need to understand the link, and how to use a knowledge of social processes when designing the more concrete process of developing and implementing best practice monitoring and benchmarking with groups.

This paper reflects on the experience of establishing and working with farmer groups as they go through a process of identifying environmental issues, setting and monitoring environmental benchmarks and identifying and implementing sustainable farming practices to meet the benchmarks.

Building social capital in groups : facilitating skill development for natural resource management

Analysis of the experiences of four farmer groups set up to learn how to jointly manage local natural resource issues shows that the groups are going though two simultaneous processes. One builds technical competency in natural resource management and the other is the underpinning social process that allows the groups to make decisions and work collectively, which builds social capital. Natural resource management practitioners and farmers are practical people. They are likely to be more comfortable with a process that develops monitoring tools and benchmarks for natural resource management than a process of group development and social capital formation. Yet the two are intrinsically linked. This paper reflects on and analyses the experience of establishing and working with farmer groups as they go through a process of identifying environmental issues, setting and monitoring environmental benchmarks and identifying and implementing sustainable farming practices to meet the benchmarks.

Two questions emerged from the analysis. First, how do the four groups compare to other measures of effective natural resource management groups? Second, what are the characteristics of the groups that make them more or less effective and what has occurred in the groups (either before or during this project) to make them more or less effective? Social capital emerges as a key determinant of group effectiveness. Social capital is most effective when it comprises a balance of bonding and bridging networks, and includes shared values in relation to the purpose of the group.

Policy makers and extension workers need to understand the link between the two simultaneous processes occurring as people come together in groups to define and implement best practice at a local level, and how to use knowledge of social processes when designing the more concrete process of developing and implementing best practice monitoring and benchmarking with groups. An understanding of how people build social capital as they work in groups will assist with designing and facilitating group projects in a range of contexts, not only natural resource management.