Study protocol: developing a decision system for inclusive housing: applying a systematic, mixed-method quasi-experimental design

Background Identifying the housing preferences of people with complex disabilities is a much needed, but under-developed area of practice and scholarship. Despite the recognition that housing is a social determinant of health and quality of life, there is an absence of empirical methodologies that can practically and systematically involve consumers in this complex service delivery and housing design market. A rigorous process for making effective and consistent development decisions is needed to ensure resources are used effectively and the needs of consumers with complex disability are properly met. Methods/Design This 3-year project aims to identify how the public and private housing market in Australia can better respond to the needs of people with complex disabilities whilst simultaneously achieving key corporate objectives. First, using the Customer Relationship Management framework, qualitative (Nominal Group Technique) and quantitative (Discrete Choice Experiment) methods will be used to quantify the housing preferences of consumers and their carers. A systematic mixed-method, quasi-experimental design will then be used to quantify the development priorities of other key stakeholders (e.g., architects, developers, Government housing services etc.) in relation to inclusive housing for people with complex disabilities. Stakeholders randomly assigned to Group 1 (experimental group) will participate in a series of focus groups employing Analytical Hierarchical Process (AHP) methodology. Stakeholders randomly assigned to Group 2 (control group) will participate in focus groups employing existing decision making processes to inclusive housing development (e.g., Risk, Opportunity, Cost, Benefit considerations). Using comparative stakeholder analysis, this research design will enable the AHP methodology (a proposed tool to guide inclusive housing development decisions) to be tested. Discussion It is anticipated that the findings of this study will enable stakeholders to incorporate consumer housing preferences into commercial decisions. Housing designers and developers will benefit from the creation of a parsimonious set of consumer-led housing preferences by which to make informed investments in future housing and contribute to future housing policy. The research design has not been applied in the Australian research context or elsewhere, and will provide a much needed blueprint for market investment to develop viable, consumer directed inclusive housing options for people with complex disability

A comparative study of legitimation strategies in hybrid regimes

The Institutions of Politics; Design, Workings, and implications ( do not use, ended 1-1-2020

On Dialectics and Human Decency: Education in the Dock

Set against the backdrop of the contemporary crisis of capitalism and world-historical events, this article examines the advance of globalized imperialism from the perspective of a Marxist-humanist approach to pedagogy known as ‘revolutionary critical pedagogy’ enriched by liberation theology. It is written as an epistolic manifesto to the transnational capitalist class, demanding that those who willingly serve its interests reconsider their allegiance and calling for a planetary revolution in the way that we both think about capitalism and how education and religion serves to reproduce it at the peril of both students and humanity as a whole

Macrosocial determinants of population health in the context of globalization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55738/1/florey_globalization_2007.pd

A Predictive Model for MicroRNA Expressions in Pediatric Multiple Sclerosis Detection

MicroRNAs (miRNAs) are a set of short non coding RNAs that play significant regulatory roles in cells. The study of miRNA data can be of valuable support for the early diagnosis of multifactorial diseases such as pediatric Multiple Sclerosis. However the analysis of miRNA expressions poses several challenges due to high dimensionality and imbalance of data. In this paper we present a data science workflow to develop a predictive model that is intended to support the clinicians in the diagnosis of Multiple Sclerosis starting from miRNA data produced by Next-Generation Sequencing. The goal is to create an effective model able to predict the pathological condition of a patient starting from his miRNA expression profile. Based on the proposed workflow, the miRNA dataset is firstly preprocessed in order to reduce its high dimensionality (from 1287 features to 40 features) and to mitigate class imbalance. Then a classification model is learnt from data via neural network training. Results show that the model defined by using the 40 data-driven selected features achieves an overall classification accuracy of 94% on test data and overcomes the model based on 42 features selected by the experts that achieves only 83% of overall accuracy