154 resultados para evidence-based approach


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The aim of the research was to explore whether the Good Lives Model (GLM) of offender rehabilitation could enhance the manner in which forensic rehabilitation is undertaken. The results were encouraging but suggested that greater attention to the wider system is required for full and effective implementation of the GLM.

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This paper presents a new Fuzzy Inference System (FIS)-based Risk Priority Number (RPN) model for the prioritization of failures in Failure Mode and Effect Analysis (FMEA). In FMEA, the monotonicity property of the RPN scores is important. To maintain the monotonicity property of an FIS-based RPN model, a complete and monotonically-ordered fuzzy rule base is necessary. However, it is impractical to gather all (potentially a large number of) fuzzy rules from FMEA users. In this paper, we introduce a new two-stage approach to reduce the number of fuzzy rules that needs to be gathered, and to satisfy the monotonicity property. In stage-1, a Genetic Algorithm (GA) is used to search for a small set of fuzzy rules to be gathered from FMEA users. In stage-2, the remaining fuzzy rules are deduced approximately by a monotonicity-preserving similarity reasoning scheme. The monotonicity property is exploited as additional qualitative information for constructing the FIS-based RPN model. To assess the effectiveness of the proposed approach, a real case study with information collected from a semiconductor manufacturing plant is conducted. The outcomes indicate that the proposed approach is effective in developing an FIS-based RPN model with only a small set of fuzzy rules, which is able to satisfy the monotonicity property for prioritization of failures in FMEA.

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Better managing diabetes has become a global priority, especially given the exponential increase in the number of diabetes patients and the financial implications of treating this silent epidemic. In this paper, we focus on how it might be possible to use a mobile technology solution to support and enable superior diabetes monitoring and management. To test this solution, we examined the context of gestational diabetes and adopted a non-blinded randomized control trial with two-arm cross over applied to a private hospital in Victoria, Australia. Further, we use an accountable care system as the theoretical lens and, from this, develop a conceptual framework to bridge evidence-based management with technologies. Theoretically, we unpack McCleallan, McKethan, Lewis, Roski, and Fisher’s (2010) study with our conceptual framework that comprises providers for information (evidence-based management) and technology (smartphone). We enhance Muhlestein, Croshaw, Merrill, Pena, and James’ (2013) accountable care paradigm with three concepts: 1) quality of life, 2) evidence-based management, and 3) affordable care. From the perspective of practice, far-reaching implications have arisen particularly for hospital management pertaining to the cost and quality of care issues. In particular, it appears that adapting mobile technology solutions such as smartphones to support various aspects of care and patient-clinician interactions is a prudent choice to minimize costs and yet provide highquality care.

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BACKGROUND: There has been a substantial body of research examining feedback practices, yet the assessment and feedback landscape in higher education is described as 'stubbornly resistant to change'. The aim of this paper is to present a case study demonstrating how an entire programme's assessment and feedback practices were re-engineered and evaluated in line with evidence from the literature in the interACT (Interaction and Collaboration via Technology) project. METHODS: Informed by action research the project conducted two cycles of planning, action, evaluation and reflection. Four key pedagogical principles informed the re-design of the assessment and feedback practices. Evaluation activities included document analysis, interviews with staff (n = 10) and students (n = 7), and student questionnaires (n = 54). Descriptive statistics were used to analyse the questionnaire data. Framework thematic analysis was used to develop themes across the interview data. RESULTS: InterACT was reported by students and staff to promote self-evaluation, engagement with feedback and feedback dialogue. Streamlining the process after the first cycle of action research was crucial for improving engagement of students and staff. The interACT process of promoting self-evaluation, reflection on feedback, feedback dialogue and longitudinal perspectives of feedback has clear benefits and should be transferable to other contexts. CONCLUSIONS: InterACT has involved comprehensive re-engineering of the assessment and feedback processes using educational principles to guide the design taking into account stakeholder perspectives. These principles and the strategies to enact them should be transferable to other contexts.

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Applying research to guide evidence-based practice is an ongoing and significant challenge for public health. Developments in the emerging field of ‘translation’ have focused on different aspects of the problem, resulting in competing frameworks and terminology. In this paper the scope of ‘translation’ in public health is defined, and four related but conceptually different ‘translation processes’ that support evidence-based practice are outlined: (1) reviewing the transferability of evidence to new settings, (2) translation research, (3) knowledge translation, and (4) knowledge translation research. Finally, an integrated framework is presented to illustrate the relationship between these domains, and priority areas for further development and empirical research are identified.

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 Implementation of a care bundle for nursing management of patients with severe traumatic brain injury was feasible in the Thai context. Use of an evidence-based care bundle increased emergency nurses’ knowledge regarding severe TBI management and improved the care delivered during the initial emergency nursing management of these patients.

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BACKGROUND: There has been a recent proliferation in the development of smartphone applications (apps) aimed at modifying various health behaviours. While interventions that incorporate behaviour change techniques (BCTs) have been associated with greater effectiveness, it is not clear to what extent smartphone apps incorporate such techniques. The purpose of this study was to investigate the presence of BCTs in physical activity and dietary apps and determine how reliably the taxonomy checklist can be used to identify BCTs in smartphone apps.

METHODS: The top-20 paid and top-20 free physical activity and/or dietary behaviour apps from the New Zealand Apple App Store Health & Fitness category were downloaded to an iPhone. Four independent raters user-tested and coded each app for the presence/absence of BCTs using the taxonomy of behaviour change techniques (26 BCTs in total). The number of BCTs included in the 40 apps was calculated. Krippendorff's alpha was used to evaluate interrater reliability for each of the 26 BCTs.

RESULTS: Apps included an average of 8.1 (range 2-18) techniques, the number being slightly higher for paid (M = 9.7, range 2-18) than free apps (M = 6.6, range 3-14). The most frequently included BCTs were "provide instruction" (83% of the apps), "set graded tasks" (70%), and "prompt self-monitoring" (60%). Techniques such as "teach to use prompts/cues", "agree on behavioural contract", "relapse prevention" and "time management" were not present in the apps reviewed. Interrater reliability coefficients ranged from 0.1 to 0.9 (Mean 0.6, SD = 0.2).

CONCLUSIONS: Presence of BCTs varied by app type and price; however, BCTs associated with increased intervention effectiveness were in general more common in paid apps. The taxonomy checklist can be used by independent raters to reliably identify BCTs in physical activity and dietary behaviour smartphone apps.

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This chapter presents an unbalanced multi-phase optimal power flow (UMOPF) based planning approach to determine the optimum capacities of multiple distributed generation units in a distribution network. An adaptive weight particle swarm optimization algorithm is used to find the global optimum solution. To increase the efficiency of the proposed scheme, a co-simulation platform is developed. Since the proposed method is mainly based on the cost optimization, variations in loads and uncertainties within DG units are also taken into account to perform the analysis. An IEEE 123 node distribution system is used as a test distribution network which is unbalanced and multi-phase in nature, for the validation of the proposed scheme. The superiority of the proposed method is investigated through the comparisons of the results obtained that of a Genetic Algorithm based OPF method. This analysis also shows that the DG capacity planning considering annual load and generation uncertainties outperform the traditional well practised peak-load planning.

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This paper is concerned with the problem of passivity analysis of neural networks with an interval time-varying delay. Unlike existing results in the literature, the time-delay considered in this paper is subjected to interval time-varying without any restriction on the rate of change. Based on novel refined Jensen inequalities and by constructing an improved Lyapunov-Krasovskii functional (LKF), which fully utilizes information of the neuron activation functions, new delay-dependent conditions that ensure the passivity of the network are derived in terms of tractable linear matrix inequalities (LMIs) which can be effectively solved by various computational tools. The effectiveness and improvement over existing results of the proposed method in this paper are illustrated through numerical examples.

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The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, most methods proposed so far are unsuitable for large networks because of their computational cost and the complex spatiotemporal diffusion processes. In this paper, we develop a community structure based approach to efficiently identify diffusion sources in large networks. We first detect the community structure of a network and assign sensors on community bridge nodes to record diffusion dynamics. From the infection time of bridge sensors, we can determine the very first infected community from which the diffusion started and spread out to other communities. This, therefore, overcomes the scalability issue in source identification problems by narrowing the set of suspects down to the first infected community. Then, to accurately locate the diffusion source from suspects, we utilize an intrinsic feature of diffusion sources that the relative infection time of any node is linear with its effective distance from the diffusion source. Thus, for each suspect, we compute the correlation coefficient to measure the degree of linear dependence between sensors' relative infection times and their effective distances from the suspect, and consider the one with the greatest correlation coefficient as the source. We evaluate our approach in two large networks containing more than 300,000 nodes, which are collected from Twitter. The experiment results show that our method can identify diffusion sources with very high degree of accuracy. Especially when the average community size shrinks, the accuracy of our approach increases dramatically.

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Precise estimation of solar energy on building roofs plays a critical role in sustainable development and renewable energy consumption of high-density human habitats. Conventional solar radiation models based on costly Light Detection and Ranging (LiDAR) data are only adequate for existing buildings, not for future construction areas. In this paper, a pixel-based methodology is constructed for estimating solar energy potential over roofs. Buildings with flat roofs in a newly planned construction area are chosen as a case study. The solar radiation at a certain cell is mathematically formulated in the pixel unit, and its yields over a certain time period are calculated by considering multiple instantaneous solar irradiances and are visually presented by image processing. Significant spatial and temporal variations in solar radiation are measured. Within the study area, the maximum and minimum annual radiation yields are estimated at 4717.72 MJ/m2/year and 342.58 MJ/m2/year respectively. Radiation contour lines are then mapped for outlining installation ranges of various solar devices. For each apartment building, around 20% of roof areas can obtain 4500 MJ/m2/year or more solar radiation yields. This study will benefit energy investors and urban planners in accurately predicting solar radiation potential and identifying regions with high radiation over building roofs. The results can be utilised in government policies and urban planning to raise awareness of the use of renewable energy sources.

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Large and persistent gaps in subnational public expenditure have important implications regarding growth, equity, and migration. In this context, we revisit the question of expenditure convergence across the American states to provide more nuanced evidence than found by a small number of previous studies. We employ a methodology due to Smeekes (Bootstrap sequential tests to determine the stationary units in a panel, 2011) that sequentially tests for unit roots in pairwise (real per capita) expenditure gaps based on user specified fractions. In a panel of 48 combined state–local government units (1957–2008), we found that expenditures on highways, sanitation, utility, and education were far more convergent than expenditures on health and hospitals, police and fire protection, and public welfare. There was little evidence of “club convergence” based on the proportion of intraregional convergent pairs. Several historically high-grant receiving states showed relatively strong evidence of convergence. Our results bode well for future output convergence and opportunities for Tiebout-type migration across jurisdictions. They also imply a diminished role for public infrastructure and education spending in business location choices over time and a mixed role for federal grants in inducing convergence.

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As with any new relationship when teachers implement a different or unfamiliar pedagogical approach into the classroom it is often accompanied by a period of unaccustomed behaviour; such as a reluctance for teachers to commit or a wariness from learners to invest or reveal. The challenges that teachers and learners face in the initial stages of this new relationship, if overcome, can be the mainstays of a mutually beneficial learning experience. Yet, as many teachers have experienced, the reverse of this is also true. Failure to adequately invest, plan and commit to the introduction of a new pedagogical approach can bring with it long term consequences that include an unwillingness to ever start a new relationship again.