Making sense of partnerships: a study of police and housing department collaboration for tackling drug and related problems on public housing estates

In recent years, interdepartmental partnerships within the public sector have been heralded as effective instruments for addressing complex social problems. For example, it is claimed that they can motivate staff and facilitate innovative practices that lead to improvements in service delivery. However, though partnerships are widely promoted as a panacea within the sector, there is a paucity of research that has looked at them from a critical perspective or examined how they operate in practice. The aim of this project is to explore the realities of partnership working by focusing on collaboration between the police and housing departments to tackle problems associated with illicit drug activity and anti-social behaviour (ASB) on three Australian public housing estates. The rationale for the project is that, though only a small minority of tenants are perpetrators, their actions can seriously blight the lives of their neighbours. Usually it is the housing department that responds to complaints relating to ASB but, in the more serious cases that are deemed criminal, the police also perform a role. Three locations were chosen as case studies. East Devonport in Tasmania and Girrawheen in Western Australia are areas with a large public housing stock. In East Devonport ASB and illicit drug activities are seen as nascent problems while in Girrawheen these problems are viewed as more pervasive, but to date there has been only limited collaboration between the police and housing departments. Collingwood in Victoria includes a large system-built housing estate that has become a location for the buying and selling of drugs. In Collingwood, police and housing departments have, for many years, worked on a series of partnership projects to address the problem and to enhance community wellbeing. The empirical component of the project entailed two stages. The fi rst stage initiated meetings between the police and housing departments in order for them to agree to a memorandum of understanding (MoU) on partnership protocols. The second stage reported on existing and new activities undertaken by both departments over a twelve month period. The research methods entailed participant observation, secondary data collection and qualitative interviews with key personnel. The fi ndings from the case studies are structured around three thematic areas: setting up partnerships, the benefi ts of partnerships, and the obstacles that can impact on partnership working

Development and inter-rater reliability of the Liverpool adverse drug reaction causality assessment tool.

To develop and test a new adverse drug reaction (ADR) causality assessment tool (CAT)

Comparison of early and late depression after TBI; (the SHEFBIT study)

OBJECTIVE: Evaluate the prevalence and risk factors for depression at 1 year after traumatic brain injury (TBI) and contrast against those at 10 weeks. METHODS: Prospective TBI admissions over 2 years to an emergency department were recruited to form a representative TBI population. Depression was recorded at 10 weeks and 1 year by HADS (Hospital Anxiety and Depression Scale) with score>8. Demographic and injury features were analyzed for association with depression. RESULTS: A total of 774 individuals were recruited of whom 690 attended  1-year follow-up; 38 had died. Only 6% of the cohort was lost to follow-up. The prevalence of depression at 10 weeks was 56.3% [95% CI 52.8-59.8] and at 1 year 41.2% [95% CI 37.6-44.9] A multivariable analysis identified the independent predictors of 1 year depression as an abnormal CT scan, past psychiatric history, alcohol intoxication, and female gender. TBI severity, age, etiology, and medical comorbidity were not significant. By contrast at 10weeks, increasing severity and CT findings were highly significant. CONCLUSIONS: Depression at 1 year post-TBI remains high but injury features are less predictive than early after injury. It is likely that pre-injury personality and coping mechanisms are more important in determining long term outcome. The predictors identified may allow targeting of vulnerable sub-populations

Quantifying similarity of pore-geometry in nanoporous materials

In most applications of nanoporous materials the pore structure is as important as the chemical composition as a determinant of performance. For example, one can alter performance in applications like carbon capture or methane storage by orders of magnitude by only modifying the pore structure. For these applications it is therefore important to identify the optimal pore geometry and use this information to find similar materials. However, the mathematical language and tools to identify materials with similar pore structures, but different composition, has been lacking. We develop a pore recognition approach to quantify similarity of pore structures and classify them using topological data analysis. This allows us to identify materials with similar pore geometries, and to screen for materials that are similar to given top-performing structures. Using methane storage as a case study, we also show that materials can be divided into topologically distinct classes requiring different optimization strategies

A Human Development Framework for CO2 Reductions

Although developing countries are called to participate in CO2 emission reduction efforts to avoid dangerous climate change, the implications of proposed reduction schemes in human development standards of developing countries remain a matter of debate. We show the existence of a positive and time-dependent correlation between the Human Development Index (HDI) and per capita CO2 emissions from fossil fuel combustion. Employing this empirical relation, extrapolating the HDI, and using three population scenarios, the cumulative CO2 emissions necessary for developing countries to achieve particular HDI thresholds are assessed following a Development As Usual approach (DAU). If current demographic and development trends are maintained, we estimate that by 2050 around 85% of the world's population will live in countries with high HDI (above 0.8). In particular, 300Gt of cumulative CO2 emissions between 2000 and 2050 are estimated to be necessary for the development of 104 developing countries in the year 2000. This value represents between 20% to 30% of previously calculated CO2 budgets limiting global warming to 2{\deg}C. These constraints and results are incorporated into a CO2 reduction framework involving four domains of climate action for individual countries. The framework reserves a fair emission path for developing countries to proceed with their development by indexing country-dependent reduction rates proportional to the HDI in order to preserve the 2{\deg}C target after a particular development threshold is reached. Under this approach, global cumulative emissions by 2050 are estimated to range from 850 up to 1100Gt of CO2. These values are within the uncertainty range of emissions to limit global temperatures to 2{\deg}C.Comment: 14 pages, 7 figures, 1 tabl

How many crowdsourced workers should a requester hire?

Recent years have seen an increased interest in crowdsourcing as a way of obtaining information from a potentially large group of workers at a reduced cost. The crowdsourcing process, as we consider in this paper, is as follows: a requester hires a number of workers to work on a set of similar tasks. After completing the tasks, each worker reports back outputs. The requester then aggregates the reported outputs to obtain aggregate outputs. A crucial question that arises during this process is: how many crowd workers should a requester hire? In this paper, we investigate from an empirical perspective the optimal number of workers a requester should hire when crowdsourcing tasks, with a particular focus on the crowdsourcing platform Amazon Mechanical Turk. Specifically, we report the results of three studies involving different tasks and payment schemes. We find that both the expected error in the aggregate outputs as well as the risk of a poor combination of workers decrease as the number of workers increases. Surprisingly, we find that the optimal number of workers a requester should hire for each task is around 10 to 11, no matter the underlying task and payment scheme. To derive such a result, we employ a principled analysis based on bootstrapping and segmented linear regression. Besides the above result, we also find that overall top-performing workers are more consistent across multiple tasks than other workers. Our results thus contribute to a better understanding of, and provide new insights into, how to design more effective crowdsourcing processes