Public Perceptions of Corruption in Tanzania: How Does the Corruption Perceived by a Sample of People in Arusha, Tanzania Compare to Tanzania’s Transparency International CPI Rating of 3.2 and What Does It Mean for Tanzania?

The World Bank defines corruption as “the abuse of public power for private benefit.” The definition of corruption can have many permutations, however, and acts of corruption are rarely recorded, thus it is difficult to quantify the level of graft in a town, city, or nation. In response to this problem, non-governmental agencies like Transparency International proposed using questionnaire-based surveys to measure public perceptions of corruption. This particular study focuses on trying to measure perceptions of corruption in Arusha, Tanzania. Arusha is a major metropolitan area in Tanzania, located in the northeastern part of the nation. Using Arushans as the sample population, this study describes how a non-representative sample of Tanzanians perceives and is affected by corruption in their country. Tanzanians gave insights on what corruption is, where it comes from, if it will continue, and described any personal experience they may have had with corruption. The results give a unique point of view on how Tanzania’s Corruption Perception Index rating of 3.2 translates to public perception of corruption. Data was collected on April 8 to April 28, 2011 via oral interviews, with the help of a Kiswahili translator. The sample population was a randomly chosen sample of all buyers within the Central Market during the time of my study. Once this data was collected, I analyzed it using descriptive statistics and used the findings to create a picture of my samples’ perceptions of corruption in Tanzania. In total, 84.4% of my sample population of 180 short-interviews in Arusha, Tanzania responded that corruption exists in Tanzania. Of this 84.4%, 96.2% said that corruption impedes economic growth in Tanzania, and 72% admitted to having a personal experience with corruption. In addition, 54.4% of the total sample population thought that corruption would either continue or emerge in Tanzania in the future. The results indicate that corruption is rampant throughout Tanzania and affects the nation on multiple levels. Yet, the future of corruption in Tanzania remains in question and unfortunately, taming the lion and putting an end to corruption will not be an easy task

Decision makers\u27 experience of participatory dynamic simulation modelling: Methods for public health policy

Background: Systems science methods such as dynamic simulation modelling are well suited to address questions about public health policy as they consider the complexity, context and dynamic nature of system-wide behaviours. Advances in technology have led to increased accessibility and interest in systems methods to address complex health policy issues. However, the involvement of policy decision makers in health-related simulation model development has been lacking. Where end-users have been included, there has been limited examination of their experience of the participatory modelling process and their views about the utility of the findings. This paper reports the experience of end-user decision makers, including senior public health policy makers and health service providers, who participated in three participatory simulation modelling for health policy case studies (alcohol related harm, childhood obesity prevention, diabetes in pregnancy), and their perceptions of the value and efficacy of this method in an applied health sector context. Methods: Semi-structured interviews were conducted with end-user participants from three participatory simulation modelling case studies in Australian real-world policy settings. Interviewees were employees of government agencies with jurisdiction over policy and program decisions and were purposively selected to include perspectives at different stages of model development. Results: The ‘co-production’ aspect of the participatory approach was highly valued. It was reported as an essential component of building understanding of the modelling process, and thus trust in the model and its outputs as a decision-support tool. The unique benefits of simulation modelling included its capacity to explore interactions of risk factors and combined interventions, and the impact of scaling up interventions. Participants also valued simulating new interventions prior to implementation in the real world, and the comprehensive mapping of evidence and its gaps to prioritise future research. The participatory aspect of simulation modelling was time and resource intensive and therefore most suited to high priority complex topics with contested options for intervening. Conclusion: These findings highlight the value of a participatory approach to dynamic simulation modelling to support its utility in applied health policy settings

The Irish Carnegie Community Engagement Classification Pilot: A critical analysis on culture and context from a community of practice approach

This article provides a reflective critique of the process undertaken to pilot the Carnegie Community Engagement Framework in Ireland between 2015 and 2016. Of particular interest to the authors is the cultural specificity of employing a US-centric self-assessment data capturing tool in a heterogeneous Irish context. Taking the reader through from conception of the idea to its execution and post-pilot reflections, we examine the cultural appropriateness and translatability of the tool to Irish higher education. To frame the discussion of the process, we employ the concept of a community of practice, as defined by Wenger (1998). This was adopted to promote a culture of collaboration in an ever-growing neoliberal system that promotes competition between institutions, rather than facilitating their co-construction of knowledge. In the analysis, we demonstrate how forming this community of practice allowed for a cohesive assessment of the challenges and opportunities that arose through the pilot process. This was particularly important since each participating institution had different motivations for engaging with the pilot. Reflecting with some distance, we consider the value that comes from operating as a community of practice, as well as some shortcomings that we identified as specific to this pilot

Simulation modelling as a tool for knowledge mobilisation in health policy settings: a case study protocol

Background: Evidence-informed decision-making is essential to ensure that health programs and services are effective and offer value for money; however, barriers to the use of evidence persist. Emerging systems science approaches and advances in technology are providing new methods and tools to facilitate evidence-based decision-making. Simulation modelling offers a unique tool for synthesising and leveraging existing evidence, data and expert local knowledge to examine, in a robust, low risk and low cost way, the likely impact of alternative policy and service provision scenarios. This case study will evaluate participatory simulation modelling to inform the prevention and management of gestational diabetes mellitus (GDM). The risks associated with GDM are well recognised; however, debate remains regarding diagnostic thresholds and whether screening and treatment to reduce maternal glucose levels reduce the associated risks. A diagnosis of GDM may provide a leverage point for multidisciplinary lifestyle modification interventions. This research will apply and evaluate a simulation modelling approach to understand the complex interrelation of factors that drive GDM rates, test options for screening and interventions, and optimise the use of evidence to inform policy and program decision-making. Methods/Design: The study design will use mixed methods to achieve the objectives. Policy, clinical practice and research experts will work collaboratively to develop, test and validate a simulation model of GDM in the Australian Capital Territory (ACT). The model will be applied to support evidence-informed policy dialogues with diverse stakeholders for the management of GDM in the ACT. Qualitative methods will be used to evaluate simulation modelling as an evidence synthesis tool to support evidence-based decision-making. Interviews and analysis of workshop recordings will focus on the participants’ engagement in the modelling process; perceived value of the participatory process, perceived commitment, influence and confidence of stakeholders in implementing policy and program decisions identified in the modelling process; and the impact of the process in terms of policy and program change. Discussion: The study will generate empirical evidence on the feasibility and potential value of simulation modelling to support knowledge mobilisation and consensus building in health settings

Turning conceptual systems maps into dynamic simulation models: An Australian case study for diabetes in pregnancy

Background: System science approaches are increasingly used to explore complex public health problems. Quantitative methods, such as participatory dynamic simulation modelling, can mobilise knowledge to inform health policy decisions. However, the analytic and practical steps required to turn collaboratively developed, qualitative system maps into rigorous and policy relevant quantified dynamic simulation models are not well described. This paper reports on the processes, interactions and decisions that occurred at the interface between modellers and end-user participants in an applied health sector case study focusing on diabetes in pregnancy. Methods: An analysis was conducted using qualitative data from a participatory dynamic simulation modelling case study in an Australian health policy setting. Recordings of participatory model development workshops and subsequent meetings were analysed and triangulated with field notes and other written records of discussions and decisions. Case study vignettes were collated to illustrate the deliberations and decisions made throughout the model development process. Results: The key analytic objectives and decision-making processes included: defining the model scope; analysing and refining the model structure to maximise local relevance and utility; reviewing and incorporating evidence to inform model parameters and assumptions; focusing the model on priority policy questions; communicating results and applying the models to policy processes. These stages did not occur sequentially; the model development was cyclical and iterative with decisions being re-visited and refined throughout the process. Storytelling was an effective strategy to both communicate and resolve concerns about the model logic and structure, and to communicate the outputs of the model to a broader audience. Conclusion: The in-depth analysis reported here examined the application of participatory modelling methods to move beyond qualitative conceptual mapping to the development of a rigorously quantified and policy relevant, complex dynamic simulation model. The analytic objectives and decision-making themes identified provide guidance for interpreting, understanding and reporting future participatory modelling projects and methods

Knowledge mobilisation for policy development: Implementing systems approaches through participatory dynamic simulation modelling

Background: Evidence-based decision-making is an important foundation for health policy and service planning decisions, yet there remain challenges in ensuring that the many forms of available evidence are considered when decisions are being made. Mobilising knowledge for policy and practice is an emergent process, and one that is highly relational, often messy and profoundly context dependent. Systems approaches, such as dynamic simulation modelling can be used to examine both complex health issues and the context in which they are embedded, and to develop decision support tools. Objective: This paper reports on the novel use of participatory simulation modelling as a knowledge mobilization tool in Australian real-world policy settings. We describe how this approach combined systems science methodology and some of the core elements of knowledge mobilisation best practice. We describe the strategies adopted in three case studies to address both technical and socio-political issues, and compile the experiential lessons derived. Finally, we consider the implications of these knowledge mobilisation case studies and provide evidence for the feasibility of this approach in policy development settings. Conclusion: Participatory dynamic simulation modelling builds on contemporary knowledge mobilisation approaches for health stakeholders to collaborate and explore policy and health service scenarios for priority public health topics. The participatory methods place the decision-maker at the centre of the process and embed deliberative methods and co-production of knowledge. The simulation models function as health policy and programme dynamic decision support tools that integrate diverse forms of evidence, including research evidence, expert knowledge and localized contextual information. Further research is underway to determine the impact of these methods on health service decision-making

High Temperature Proton Exchange Membranes With Enhanced Proton Conductivities At Low Humidity and High Temperature Based On Polymer Blends and Block Copolymers of Poly(1,3-Cyclohexadiene) and Poly(ethylene Glycol)

Hot (at 120 °C) and dry (20% relative humidity) operating conditions benefit fuel cell designs based on proton exchange membranes (PEMs) and hydrogen due to simplified system design and increasing tolerance to fuel impurities. Presented are preparation, partial characterization, and multi-scale modeling of such PEMs based on cross-linked, sulfonated poly(1,3-cyclohexadiene) (xsPCHD) blends and block copolymers with poly(ethylene glycol) (PEG). These low cost materials have proton conductivities 18 times that of current industry standard Nafion at hot, dry operating conditions. Among the membranes studied, the blend xsPCHD-PEG PEM displayed the highest proton conductivity, which exhibits a morphology with higher connectivity of the hydrophilic domain throughout the membrane. Simulation and modeling provide a molecular level understanding of distribution of PEG within this hydrophilic domain and its relation to proton conductivities. This study demonstrates enhancement of proton conductivity at high temperature and low relative humidity by incorporation of PEG and optimized sulfonation conditions