Nambour : the model rural school

This paper examines the Rural Schools of Queensland. Starting with Nambour in 1917, the scheme incorporated thirty schools, and operated for over forty years. The rhetoric of the day was that boys and girls from the senior classes of primary school would be provided with elementary instruction of a practical character. In reality, the subjects taught were specifically tailored to provide farm skills to children in rural centres engaged in farming, dairying or fruit growing. Linked to each Rural School was a number of smaller surrounding schools, students from which travelled to the Rural School for special agricultural or domestic instruction. Through this action, the Queensland Department of Public Instruction left no doubt it intended to provide educational support for agrarian change and development within the state; in effect, they had set in motion the creation of a Queensland yeoman class. The Department’s intention was to arrest or reverse the trend toward urbanisation — whilst increasing agricultural productivity — through the making of a farmer born of the land and accepting of the new scientific advances in agriculture.

School-based programs for increasing connectedness and reducing risk behavior : a systematic review

School connectedness has a significant impact on adolescent outcomes, including reducing risk taking behavior. This paper critically examines the literature on school-based programs targeting increased connectedness for reductions in risk taking. Fourteen articles describing seven different school-based programs were reviewed. Programs drew on a range of theories to increase school connectedness, and evaluations conducted for the majority of programs demonstrated positive changes in school connectedness, risk behavior, or a combination of the two. Many of the reviewed programs involved widespread school system change, however, which is frequently a complex and time consuming task. Future research is needed to examine the extent of intervention complexity required to result in change. This review also showed a lack of consistency in definitions and measurement of connectedness as well as few mediation analyses testing assumptions of impact on risk taking behavior through increases in school connectedness. Additionally, this review revealed very limited evaluation of the elements of multi-component programs that are most effective in increasing school connectedness and reducing adolescent risk taking.

General belongingness, workplace belongingness, and depressive symptoms

Research has shown that a strong relationship exists between belongingness and depressive symptoms; however, the contribution of specific types of belongingness remains unknown. Participants (N=369) completed the sense of belonging instrument, psychological sense of organizational membership, and the depression scale of the depression anxiety stress scales. Factor analysis demonstrated that workplace and general belongingness are distinct constructs. When regressed onto depressive symptoms, these belongingness types made independent contributions, together accounting for 45% of variance, with no moderation effects evident. Hence, general belongingness and specific workplace belongingness appear to have strong additive links to depressive symptoms. These results add support to the belongingness hypothesis and sociometer theory and have significant implication for depression prevention and treatment

On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations

Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.) The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.

Association between ambient ultraviolet radiation and risk of esophageal cancer

OBJECTIVES: Ecological studies have suggested an inverse relationship between latitude and risks of some cancers. However, associations between solar ultraviolet radiation (UVR) exposure and esophageal cancer risk have not been fully explored. We therefore investigated the association between nevi, freckles, and measures of ambient UVR over the life-course with risks of esophageal cancers. METHODS: We compared estimated lifetime residential ambient UVR among Australian patients with esophageal cancer (330 esophageal adenocarcinoma (EAC), 386 esophago-gastric junction adenocarcinoma (EGJAC), and 279 esophageal squamous cell carcinoma (ESCC)), and 1471 population controls. We asked people where they had lived at different periods of their life, and assigned ambient UVR to each location based on measurements from NASA's Total Ozone Mapping Spectrometer database. Freckling and nevus burden were self-reported. We used multivariable logistic regression models to estimate the magnitude of associations between phenotype, ambient UVR, and esophageal cancer risk. RESULTS: Compared with population controls, patients with EAC and EGJAC were less likely to have high levels of estimated cumulative lifetime ambient UVR (EAC odds ratio (OR) 0.59, 95% confidence interval (CI) 0.35-0.99, EGJAC OR 0.55, 0.34-0.90). We found no association between UVR and risk of ESCC (OR 0.91, 0.51-1.64). The associations were independent of age, sex, body mass index, education, state of recruitment, frequency of reflux, smoking status, alcohol consumption, and H. pylori serostatus. Cases with EAC were also significantly less likely to report high levels of nevi than controls. CONCLUSIONS: These data show an inverse association between ambient solar UVR at residential locations and risk of EAC and EGJAC, but not ESCC.

Licensing age issues : deliberations from a workshop devoted to this topic

Objective: To highlight the issues and discuss the research evidence regarding safety, mobility, and other consequences of different licensing ages. Methods: Information included is based on presentations and discussions at a one-day workshop on licensing age issues, and a review and synthesis of the international literature. Results: The literature indicates that higher licensing ages are associated with safety benefits. There is an associated mobility loss, more likely to be an issue in rural states. Legislative attempts to raise the minimum age for independent driving in the United States, e.g., from 16 to 17, have been resisted, although in some states the age has been raised indirectly through graduated driver licensing (GDL) policies. Conclusions: Jurisdictions can achieve reductions in teenage crashes by raising the licensing age. This can be done directly, or indirectly by strengthening GDL systems, in particular extending the minimum length of the learner period.

Malnutrition and poor food intake are associated with prolonged hospital stay, frequent readmissions, and greater in-hospital mortality: Results from the Nutrition Care Day Survey 2010

Background & aims The Australasian Nutrition Care Day Survey (ANCDS) ascertained if malnutrition and poor food intake are independent risk factors for health-related outcomes in Australian and New Zealand hospital patients. Methods Phase 1 recorded nutritional status (Subjective Global Assessment) and 24-h food intake (0, 25, 50, 75, 100% intake). Outcomes data (Phase 2) were collected 90-days post-Phase 1 and included length of hospital stay (LOS), readmissions and in-hospital mortality. Results Of 3122 participants (47% females, 65 ± 18 years) from 56 hospitals, 32% were malnourished and 23% consumed ≤ 25% of the offered food. Malnourished patients had greater median LOS (15 days vs. 10 days, p < 0.0001) and readmissions rates (36% vs. 30%, p = 0.001). Median LOS for patients consuming ≤ 25% of the food was higher than those consuming ≤ 50% (13 vs. 11 days, p < 0.0001). The odds of 90-day in-hospital mortality were twice greater for malnourished patients (CI: 1.09–3.34, p = 0.023) and those consuming ≤ 25% of the offered food (CI: 1.13–3.51, p = 0.017), respectively. Conclusion The ANCDS establishes that malnutrition and poor food intake are independently associated with in-hospital mortality in the Australian and New Zealand acute care setting.

Effect of attitudes, subjective norms and perceived control on nurses' intention to assess patients' pain

This study examines the underlying determinants of nurses' behaviour regarding the conduct of pain assessments. One hundred nurses in a variety of health care facilities were invited to complete an Attitude Intention Questionnaire based upon the theory of planned action which is an extension of the theory of reasoned action. Results provide some support for the theory of planned action, as nurses' intention to conduct pain assessment was shown to be predicted by attitude, subjective norms and perceived control, although the latter was the only variable to make an independent contribution to intention. Additional support for the importance of perceived control was provided by the analysis of 'intenders' and 'non-intenders' (to conduct pain assessments), as perceived control was the only variable which differed significantly between the groups. The findings are consistent with earlier studies which showed that the variables in the theory of planned behaviour provided reasonably accurate predictions of behavioural intention.

What can grassroots leadership teach us about school leadership

This paper explores grassroots leadership, an under-researched and often side-lined approach to leadership that operates outside of formal bureaucratic structures. The paper’s central purpose is the claim that an understanding of grassroots leadership and tactics used by grassroots leaders provides valuable insights for the study of school leadership. In this paper, we present and discuss an original model of grassroots leadership based on the argument that this under-researched area can further our understanding of school leadership. Drawing upon the limited literature in the field, we present a model consisting of two approaches to change (i.e. conflict and consensus) and two categories of change (i.e. reform and refinement) and then provide illustrations of how the model works in practice. We make the argument that the model has much merit for conceptualizing school leadership, and this is illustrated by applying the model to formal bureaucratic leadership within school contexts. Given the current climate in education where business and management language is pervasive within leadership-preparation programs, we argue that it is timely for university academics, who are responsible for preparing school leaders to consider broadening their approach by exposing school leaders to a variety of change-based strategies and tactics used by grassroots leaders.

The Yonder project : literacy and social competency development in primary school students through co-creation of live performance

Since 2007 Kite Arts Education Program (KITE), based at Queensland Performing Arts Centre (QPAC), has been engaged in delivering a series of theatre-based experiences for children in low socio-economic primary schools in Queensland. The artist in residence (AIR) project titled Yonder includes performances developed by the children with the support and leadership of teacher artists from KITE for their community and parents/carers,supported by a peak community cultural institution. In 2009,Queensland Performing Arts Centre partnered with Queensland University of Technology (QUT) Creative Industries Faculty (Drama) to conduct a three-year evaluation of the Yonder project to understand the operational dynamics, artistic outputs and the educational benefits of the project. This paper outlines the research findings for children engaged in the Yonder project in the interrelated areas of literacy development and social competencies. Findings are drawn from six iterations of the project in suburban locations on the edge of Brisbane city and in regional Queensland.

ICT driven pedagogies and its impact on learning outcomes in high school mathematics

ICT (Information and Communication Technology) creates numerous opportunities for teachers to re-think their pedagogies. In subjects like mathematics which draws upon abstract concepts, ICT creates such an opportunity. Instead of a mimetic pedagogical approach, suitably designed activities with ICT can enable learners to engage more proactively with their learning. In this quasi-experimental designed study, ICT was used in teaching mathematics to a group of first year high school students (N=25) in Australia. The control group was taught predominantly through traditional pedagogies (N=22). Most of the variables that had previously impacted on the design of such studies were suitably controlled in this yearlong investigation. Quantitative and qualitative results showed that students who were taught by ICT driven pedagogies benefitted from the experience. Pre and post-test means showed that there was a difference between the treatment and control groups. Of greater significance was that the students (in the treatment group) believed that the technology enabled them to engage more with their learning.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Introducing ICT to a primary school in a developing country : a Fijian experience

One aspect of quality education in the 21st century is the availability of digital resources in schools. Many developing countries need to build this capability – not just in terms of technology but teacher capability as well. One of the ways to achieve such capacity is through knowledge sharing between teachers and educators in developed and developing countries. Over time such collaboration can have a lasting impact on all participants on both sides of the digital divide. This paper reports on how such collaboration can occur. It focuses on the initial stages of a long-term initiative where our primary objective is to develop models, which demonstrate how we (in developed countries) can engage productively and meaningfully with schools in developing countries to build their ICT capacity. As part of this initiative, we introduced laptops and LEGO robotics tool kits to a rural primary school in Fiji. We developed ICT activities that aligned with the curriculum in a number of subjects. In addition, we worked with the teachers over two weeks to build their expertise.

Integrating robotics in primary school activities

This article outlines the integration of robotics in two settings in a primary school. This initiative was part of an Australian Research Council project which was undertaken at this school. The article highlights how robotics was integrated in a technology unit in a year four class. It also explains how it was embedded into an after-school program which catered for students from years five to seven. From these experiences further possibilities of engaging with robotics are also discussed.

Characterising metal build-up on urban road surfaces

Reliable approaches for predicting pollutant build-up are essential for accurate urban stormwater quality modelling. Based on the in-depth investigation of metal build-up on residential road surfaces, this paper presents empirical models for predicting metal loads on these surfaces. The study investigated metals commonly present in the urban environment. Analysis undertaken found that the build-up process for metals primarily originating from anthropogenic (copper and zinc) and geogenic (aluminium, calcium, iron and manganese) sources were different. Chromium and nickel were below detection limits. Lead was primarily associated with geogenic sources, but also exhibited a significant relationship with anthropogenic sources. The empirical prediction models developed were validated using an independent data set and found to have relative prediction errors of 12-50%, which is generally acceptable for complex systems such as urban road surfaces. Also, the predicted values were very close to the observed values and well within 95% prediction interval.