Learning to be drier : a case study of adult and community learning in the Australian Riverland

This article explores the adult and community learning associated with 'learning to be drier' in the Riverland region of South Australia. Communities in the Riverland are currently adjusting and making changes to their understandings and practices as part of learning to live with less water. The analysis of adult and community learning derived from this research identifies six different forms of learning. These are, learning to produce, learning to be efficient, learning to survive, learning to live with uncertainty, learning to be sustainable and learning to share. These forms of learning do not occur in isolation and separately from each other but to the contrary are occuring simultaneously with and alongside each other. Further, it is argued that the people and communities in the Riverland, through learning to live with the effects of climate change and less water, are at the forfront of learning to be drier.

A good lives model of clinical and community rehabilitation

Aims of the paper. The aim of this paper was to introduce the Good Lives Model, originally developed for offender rehabilitation, to the clinical rehabilitation community. We argue that this model has considerable promise, both as a ‘thinking tool’ and as an integrative framework emphasizing the centrality of the person in clinical and community rehabilitation for complex and chronic health conditions.

Key findings and implications. The essential features of a good rehabilitation theory are first outlined. These are the general principles and assumptions that underpin a theory, the aetiological assumptions and the intervention implications. The Good Lives Model for clinical rehabilitation is then described in terms of these three components of a good rehabilitation theory.

Conclusions and recommendations. The Good Lives Model has considerable promise as a tool for integrating many diverse aspects of current best practice in rehabilitation while maintaining the individual client as the central focus. At the same time it is provisional and further theoretical development and empirical support is required.

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.

A simultaneous extraction of context and community from pervasive signals using nested dirichlet process

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain human dynamics or behaviors and then use them as a way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows a nested structure to be built to summarize data at multiple levels. We demonstrate our framework on five datasets where the advantages of the proposed approach are validated.

A pilot program of physical activity promotion among clients receiving home and community care

Three Victorian local governments cooperated in a pilot study of physical activity promotion as part of home and community care (HACC) service delivery. Thirty-one people receiving HACC volunteered to participate, including completing the Transtheoretical Stages of Change Exercise Questionnaire and the short-form Stanford Health Assessment Questionnaire (HAQ) just before and at 3 months and 6 months after starting regular self-selected physical activity. Twenty-one participants returned questionnaires at 3 months, and 17 participants returned questionnaires at 6 months. Data were analysed using paired t tests and effect sizes were calculated as mean differences. At 3 months, mean improvements were identified on 6 of the 8 HAQDI (disability index) subscales, and in the overall HAQ-DI score. Improvement in dressing and grooming was preserved at 6 months. At either 3 or 6 months, improvements in dressing and grooming, reach, hygiene, and daily activities, and overall HAQ-DI score exceeded the minimum clinically important difference. No improvements were statistically significant, as is likely in a pilot study with a small sample, however, these results suggest that even very small increases in physical activity may afford clinically meaningful improvements in some areas of physical function required for independent living.