University of Adelaide

About: University of Adelaide is a education organization based out in Adelaide, South Australia, Australia. It is known for research contribution in the topics: Population & Pregnancy. The organization has 27251 authors who have published 79167 publications receiving 2671128 citations. The organization is also known as: The University of Adelaide & Adelaide University.

Royal Australian and New Zealand College of Psychiatrists clinical practice guidelines for the management of schizophrenia and related disorders

Abstract: Objectives:This guideline provides recommendations for the clinical management of schizophrenia and related disorders for health professionals working in Australia and New Zealand. It aims to encou...

Selecting among five common modelling approaches for integrated environmental assessment and management

Abstract: The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings. We review five common integrated modelling approaches.Model choice considers purpose, data type, scale and uncertainty treatment.We present a guiding framework for selecting the most appropriate approach.

PARIHS revisited: from heuristic to integrated framework for the successful implementation of knowledge into practice

Abstract: The Promoting Action on Research Implementation in Health Services, or PARIHS framework, was first published in 1998. Since this time, work has been ongoing to further develop, refine and test it. Widely used as an organising or conceptual framework to help both explain and predict why the implementation of evidence into practice is or is not successful, PARIHS was one of the first frameworks to make explicit the multi-dimensional and complex nature of implementation as well as highlighting the central importance of context. Several critiques of the framework have also pointed out its limitations and suggested areas for improvement. Building on the published critiques and a number of empirical studies, this paper introduces a revised version of the framework, called the integrated or i-PARIHS framework. The theoretical antecedents of the framework are described as well as outlining the revised and new elements, notably, the revision of how evidence is described; how the individual and teams are incorporated; and how context is further delineated. We describe how the framework can be operationalised and draw on case study data to demonstrate the preliminary testing of the face and content validity of the revised framework. This paper is presented for deliberation and discussion within the implementation science community. Responding to a series of critiques and helpful feedback on the utility of the original PARIHS framework, we seek feedback on the proposed improvements to the framework. We believe that the i-PARIHS framework creates a more integrated approach to understand the theoretical complexity from which implementation science draws its propositions and working hypotheses; that the new framework is more coherent and comprehensive and at the same time maintains it intuitive appeal; and that the models of facilitation described enable its more effective operationalisation.

Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

Abstract: Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Efficient piecewise training of the proposed deep structured model is then applied to avoid repeated expensive CRF inference for back propagation. For capturing the patch-background context, we show that a network design with traditional multi-scale image input and sliding pyramid pooling is effective for improving performance. Our experimental results set new state-of-the-art performance on a number of popular semantic segmentation datasets, including NYUDv2, PASCAL VOC 2012, PASCAL-Context, and SIFT-flow. In particular, we achieve an intersection-overunion score of 78:0 on the challenging PASCAL VOC 2012 dataset.