Econometric techniques for estimating construction demand in Australia

This study aims to develop reliable demand estimation models, at both national and regional levels, for the Australia’s construction market. The developed models would benefit the industry by serving as a reliable aid to policy in the areas of tendering, pricing, resource allocating, labour and workload planning.

Organizational change in the public sector: augmenting the demand control model to predict employee outcomes under New Public Management

This study identifies the environmental and personal characteristics that predict employee outcomes within an Australian public sector organization that had, under New Public Management (NPM), implemented a variety of practices traditionally found in the private sector. These are more results-oriented, and their adoption can be accompanied by increased strain for employees. The current investigation was guided by two complementary theories, the Demand Control Support (DCS) model and Conservation of Resources (COR) theory, and sought to examine the benefits of building on the DCS to include both situation-specific stressors and internal coping resources. Survey responses from 1,155 employees were analysed. The hierarchical regression analyses indicated that both external and employee-centred variables made significant contributions to variations in psychological health, job satisfaction, and organizational commitment. The external resources, work based support and, to a lesser extent, job control, predicted relatively large proportions of the variance in the target variables. The situation-specific stressors, particularly those involving harmful management practices (e.g., insufficient time to do job as well as you would like, lack of recognition for good work), made significant contributions to the outcome measures and generally supported the process of augmenting the generic components of the DCS with more situation-specific variables. In terms of internal resources, problem and emotion-based coping improved the capacity of the model to predict psychological health. The results suggest that the impact of NPM can be ameliorated by incorporating the dimensions of the augmented DCS and coping resources into the change programme.

Rural nursing and health care reforms : building a social model

This article explores recent shifts in health-care policy and the implications for rural nursing in Australia. Health-care reforms have resulted in the implementation of a 'market forces' ideology, creating tensions between economic imperatives and the need for equity and greater access in rural service delivery. New models of health-service delivery have been developed that have significant implications for the way rural health care is defined, practised and received. The issues surrounding the context of rural nursing practice and service delivery are discussed.

Optimising the life cycle energy performance of residential buildings

A holistic approach to low-energy building design is essential to ensure that any efficiency improvement strategies provide a net energy benefit over the life of the building. Previous work by the authors has established a model for informing low-energy building design based on a comparison of the life cycle energy demand associated with a broad range of building assemblies. This model ranks assemblies based on their combined initial and recurrent embodied energy and operational energy demand. The current study applies this model to an actual residential building in order to demonstrate the application of the model for optimising a building’s life cycle energy performance. The aim of this study was to demonstrate how the availability of comparable energy performance information at the building design stage can be used to better optimise a building’s energy performance. The life cycle energy demand of the case study building, located in the temperate climate of Melbourne, Australia, was quantified using a comprehensive embodied energy assessment technique and TRNSYS thermal energy simulation software. The building was then modelled with variations to its external assemblies in an attempt to optimise its life cycle energy performance. The alternative assemblies chosen were those shown through the author’s previous modelling to result in the lowest life cycle energy demand for each building element. The best performing assemblies for each of the main external building elements were then combined into a best-case scenario to quantify the potential life cycle energy savings possible compared to the original building. The study showed that significant life cycle energy savings are possible through the modelling of individual building elements for the case study building. While these findings relate to a very specific case, this study demonstrates the application of a model for optimising building life cycle energy performance that may be applied more broadly during early-stage building design to optimise life cycle energy performance.

Food demand elasticities for Australia

There is renewed interest in robust estimates of food demand elasticities at a disaggregated level not only to analyse the impact of changing food preferences on the agricultural sector, but also to establish the likely impact of pricing incentives on households. Using data drawn from two national Household Expenditure Surveys covering the periods 1998/1999 and 2003/2004, and adopting an Almost Ideal Demand System approach that addresses the zero observations problem, this paper estimates a food demand system for 15 food categories for Australia. The categories cover the standard food items that Australian households demand routinely. Own-price, cross-price and expenditure elasticity estimates of the Marshallian and Hicksian types have been derived for all categories. The parameter estimates obtained in this study represent the first integrated set of food demand elasticities based on a highly disaggregated food demand system for Australia, and all accord with economic intuition.

The work-based predictors of job engagement and job satisfaction experienced by community health professionals

Background: Job engagement represents a critical resource for community-based health care agencies to achieve high levels of effectiveness. However, studies examining the organisational sources of job engagement among health care professionals have generally overlooked those workers based in community settings.
Purpose: This study drew on the demand-control model, in addition to stressors that are more specific to community health services (e.g., unrewarding management practices), to identify conditions that are closely associated with the engagement experienced by a community health workforce. Job satisfaction was also included as a way of assessing how the predictors of job engagement differ from those associated with other job attitudes.
Methodology/Approach: Health and allied health care professionals (n = 516) from two
Australian community health services took part in the current investigation. Responses from the two organisations were pooled and analysed using linear multiple regression.
Findings: The analyses revealed that three working conditions were predictive of both job engagement and job satisfaction (i.e., job control, quantitative demands and unrewarding management practices). There was some evidence of differential effects with cognitive demands being associated with job engagement, but not job satisfaction.
Practice Implications: The results provide important insights into the working conditions that, if addressed, could play key roles in building a more engaged and satisfied community health workforce. Further, working conditions like job control and management practices are amenable to change and thus represent important areas where community health services could enhance the energetic and motivational resources of their employees.

Teachers' work hours, work-family conflict and health: the mediating effects of perceived control and organisational support

Teachers are among those working longer hours more than ever before. the implications of these long hours on teachers' health, through work-family conflict, control over hours worked and organisational support were investigated. 120 teachers, of whom 91 (59.3% female) reported
working in excess of 37 1/2 hours in the week prior, participated in the study. Long hours, work-family conflict, control and organisational support, explained 69% of the variance in health. There was no direct effect of long worked hours on health however long hours did have a direct impact on work-family conflict, organisational support, and control and, through
these, teachers' health. Work-family conflict exerted a direct negative impact on health. These findings are discussed in individual and organisational tenns.

Predicting spatio-temporal distribution for eastern Australian birds using birds Australia's atlas data : survey method, habitat and seasonal effects

Faunal atlases are landscape-level survey collections that can be used for describing spatial and temporal patterns of distribution and densities. They can also serve as a basis for quantitative analysis of factors that may influence the distributions of species. We used a subset of Birds Australia’s Atlas of Australian Birds data (January 1998 to December 2002) to examine the spatio-temporal distribution patterns of 280 selected species in eastern Australia (17–37°S and 136–152°E). Using geographical information systems, this dataset was converted into point coverage and overlaid with a vegetation polygon layer and a half-degree grid. The exploratory data analysis involved calculating species-specific reporting rates spatially, per grid and per vegetation unit, and also temporally, by month and year. We found high spatio-temporal variability in the sampling effort. Using generalised linear models on unaggregated point data, the influences of four factors – survey method and month, geographical location and habitat type – were analysed for each species. When counts of point data were attributed to grid-cells, the total number of species correlated with the total number of surveys, while the number of records per species was highly variable. Surveys had high interannual location fidelity. The predictive values of each of the four factors were species-dependent. Location and habitat were correlated and highly predictive for species with restricted distribution and strong habitat preference. Month was only of importance for migratory species. The proportion of incidental sightings was important for extremely common or extremely rare species. In conclusion, behaviour of species differed sufficiently to require building a customized model for each species to predict distribution. Simple models were effective for habitat specialists with restricted ranges, but for generalists with wide distributions even complex models gave poor predictions.

Application of computational media aesthetics methodology to extracting color semantics in film

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.

Does workplace social capital buffer the effects of job stress? A cross-sectional, multilevel analysis of cigarette smoking among U.S. manufacturing workers

Objective: To investigate whether workplace social capital buffers the association between job stress and smoking status. Methods: As part of the Harvard Cancer Prevention Project's Healthy Directions—Small Business Study, interviewer-administered questionnaires were completed by 1740 workers and 288 managers in 26 manufacturing firms (84% and 85% response). Social capital was assessed by multiple items measured at the individual level among workers and contextual level among managers. Job stress was operationalized by the demand-control model. Multilevel logistic regression was used to estimate associations between job stressors and smoking and test for effect modification by social capital measures. Results: Workplace social capital (both summary measures) buffered associations between high job demands and smoking. One compositional item—worker trust in managers—buffered associations between job strain and smoking. Conclusion: Workplace social capital may modify the effects of psychosocial working conditions on health behaviors.

Netflix Australia launch could be imminent

Netflix's success has been a phenomenon in the United States and where it has migrated as a source for the distribution of film and television content in recent years. Now producing and distributing original series such as House of Cards and Arrested Development, Netflix is building a successful model that could move into the australian market in future months.

Psychosocial and other working conditions in relation to body mass index in a representative sample of Australian workers

Background The aim of the study was to examine the relationship between psychosocial and other working conditions and body-mass index (BMI) in a working population. This study contributes to the approximately dozen investigations of job stress, which have demonstrated mixed positive and negative results in relation to obesity, overweight and BMI. Methods A cross-sectional population-based survey was conducted among working Australians in the state of Victoria. Participants were contacted by telephone from a random sample of phone book listings. Information on body mass index was self-reported as were psychosocial work conditions assessed using the demand/control and effort/reward imbalance models. Other working conditions measured included working hours, shift work, and physical demand. Separate linear regression analyses were undertaken for males and females, with adjustment for potential confounders. Results A total of 1101 interviews (526 men and 575 women) were completed. Multivariate models (adjusted for socio-demographics) demonstrated no associations between job strain, as measured using the demand/control model, or ERI using the effort/reward imbalance model (after further adjustment for over commitment) and BMI among men and women. Multivariate models demonstrated a negative association between low reward and BMI among women. Among men, multivariate models demonstrated positive associations between high effort, high psychological demand, long working hours and BMI and a negative association between high physical demand and BMI. After controlling for the effort/reward imbalance or the demand/control model, the association between physical demand and working longer hours and BMI remained. Conclusion Among men and women the were differing patterns of both exposures to psychosocial working conditions and associations with BMI. Among men, working long hours was positively associated with higher BMI and this association was partly independent of job stress. Among men physical demand was negatively associated with BMI and this association was independent of job stress.

Bayesian nonparametric multilevel clustering with group-level contexts

We present a Bayesian nonparametric framework for multilevel clustering which utilizes group- level context information to simultaneously discover low-dimensional structures of the group contents and partitions groups into clusters. Using the Dirichlet process as the building block, our model constructs a product base-measure with a nested structure to accommodate content and context observations at multiple levels. The proposed model possesses properties that link the nested Dinchiet processes (nDP) and the Dirichlet process mixture models (DPM) in an interesting way: integrating out all contents results in the DPM over contexts, whereas integrating out group-specific contexts results in the nDP mixture over content variables. We provide a Polyaurn view of the model and an efficient collapsed Gibbs inference procedure. Extensive experiments on real-world datasets demonstrate the advantage of utilizing context information via our model in both text and image domains.

GIS-based dynamic modelling and analysis of flash floods considering land-use planning

Flood inundation is a common natural disaster and a growing development challenge for many cities and thousands of small towns around the world. Soil features have frequently altered with the rapid development of urbanised regions, which has led to more frequent and longer duration of flooding in urban flood-prone regions. Thus, this paper presents a geographic information system (GIS)-based methodology for measuring and visualising the effects on urban flash floods generated by land-use changes over time. The measurement is formulated with a time series in order to perform a dynamic analysis. A catchment mesh is introduced into a hydrological model for reflecting the spatial layouts of infrastructure and structures over different construction periods. The Geelong Waurn Ponds campus of Deakin University is then selected as a case study. Based on GIS simulation and mapping technologies, this research illustrates the evolutionary process of flash floods. The paper then describes flood inundation for different built environments and presents a comparison by quantifying the flooding extents for infrastructure and structures. The results reveal that the GIS-based estimation model can examine urban flash floods in different development phases and identify the change of flooding extents in terms of land-use planning. This study will bring benefits to urban planners in raising awareness of flood impact and the approach proposed here could be used for flood mitigation through future urban planning.

iPrevent®: a tailored, web-based, decision support tool for breast cancer risk assessment and management

We aimed to develop a user-centered, web-based, decision support tool for breast cancer risk assessment and personalized risk management. Using a novel model choice algorithm, iPrevent(®) selects one of two validated breast cancer risk estimation models (IBIS or BOADICEA), based on risk factor data entered by the user. Resulting risk estimates are presented in simple language and graphic formats for easy comprehension. iPrevent(®) then presents risk-adapted, evidence-based, guideline-endorsed management options. Development was an iterative process with regular feedback from multidisciplinary experts and consumers. To verify iPrevent(®), risk factor data for 127 cases derived from the Australian Breast Cancer Family Study were entered into iPrevent(®), IBIS (v7.02), and BOADICEA (v3.0). Consistency of the model chosen by iPrevent(®) (i.e., IBIS or BOADICEA) with the programmed iPrevent(®) model choice algorithm was assessed. Estimated breast cancer risks from iPrevent(®) were compared with those attained directly from the chosen risk assessment model (IBIS or BOADICEA). Risk management interventions displayed by iPrevent(®) were assessed for appropriateness. Risk estimation model choice was 100 % consistent with the programmed iPrevent(®) logic. Discrepant 10-year and residual lifetime risk estimates of >1 % were found for 1 and 4 cases, respectively, none was clinically significant (maximal variation 1.4 %). Risk management interventions suggested by iPrevent(®) were 100 % appropriate. iPrevent(®) successfully integrates the IBIS and BOADICEA risk assessment models into a decision support tool that provides evidence-based, risk-adapted risk management advice. This may help to facilitate precision breast cancer prevention discussions between women and their healthcare providers.