11,273 research outputs found

    Implementing screening and brief Interventions for excessive alcohol consumption in primary health care

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    O consumo de bebidas alcoólicas é um dos principais fatores de risco da morbilidade e mortalidade prematura a nível mundial. As pessoas que consomem este género de bebidas têm um risco aumentado de vir a desenvolver mais de 200 problemas de saúde diferentes. A maioria do impacto do consumo de álcool na saúde humana é determinado por duas dimensões: o volume total de álcool consumido e o padrão de consumo. Existem várias medidas com comprovada eficácia que podem ser empregues para reduzir o risco associado ao consumo de álcool, entre as quais se encontra a deteção precoce e intervenção breve ao nível dos Cuidados de Saúde Primários. A maioria dos profissionais de saúde neste nível de cuidados considera o consumo de álcool como um importante problema de saúde e manifesta o seu apoio a medidas que visem reduzir o seu impacto. No entanto, poucos são os profissionais dos Cuidados de Saúde Primários que de forma sistemática identificam e aconselham os seus doentes relativamente aos seus hábitos etílicos. Como tal, o objetivo geral desta tese foi investigar como implementar a deteção precoce e intervenção breve no consumo excessivo de álcool nos Cuidados de Saúde Primários. Foi realizada uma revisão sistemática das barreiras e facilitadores à implementação da deteção precoce e intervenção breve no consumo excessivo de álcool nos Cuidados de Saúde Primários. As barreiras e facilitadores identificados nesta revisão foram analisados à luz da teoria de modificação comportamental para compreender a ligação destes fatores aos determinantes da mudança de comportamento, e para identificar as estratégias conceptualmente mais eficazes para abordar as barreiras e facilitadores à mudança de comportamento dos profissionais dos Cuidados de Saúde Primários no sentido de aumentar as taxas de deteção precoce e intervenção breve no consumo excessivo de álcool. Esta metodologia foi utilizada para desenhar um programa de implementação com base em pressupostos teóricos que foi testado num estudo experimental randomizado e controlado em clusters. Esta tese identificou diversas barreiras à implementação, ligadas a todos os domínios teóricos da mudança comportamental. As barreiras mais frequentemente mencionadas pelos profissionais foram: preocupação sobre as suas competências e eficácia para realizar a deteção precoce e intervenção breve; falta de conhecimento específico sobre o consumo de álcool; falta de tempo; falta de materiais; falta de apoio; e atitudes para com o doente com consumos excessivos de álcool. Esta tese mostrou também a existência de dois grupos distintos de médicos de família com base nas suas atitudes para com estes doentes, um com atitudes mais positivas, o outro com atitudes mais negativas. Esta tese mostrou ainda que um programa de implementação da deteção precoce e intervenção breve, desenhado com base em pressupostos teóricos de modificação comportamental, adaptado às barreiras e facilitadores da implementação, aumenta de forma significativa as taxas de identificação precoce dos consumos de álcool. Esta tese contribui para aumentar o conhecimento atual no sentido em que põe à disposição dos investigadores evidência prática sobre como abordar os fatores com influência na implementação da identificação precoce e intervenção breve para o consumo de álcool ao nível dos Cuidados de Saúde Primários. Esta tese contribui também para um melhor entendimento dos mecanismos subjacentes à resistência e à mudança de comportamento dos profissionais dos Cuidados de Saúde Primários no que respeita à implementação da deteção precoce e intervenção breve do consumo de álcool. Os resultados desta tese poderão ser usados por investigadores e decisores políticos para desenhar novos programas de implementação tendo como objetivo modificar esta prática clínica ao nível dos Cuidados de Saúde Primários.Alcohol use is among the leading risk factors for the global burden of disease and premature death. People who drink alcoholic beverages are at risk of developing more than 200 diseases and injury conditions. Most of the impact of alcohol consumption on human health and well-being is determined by two dimensions of drinking: the total volume of alcohol consumed and the pattern of drinking. Several effective strategies exist to reduce the harmful use of alcohol, which includes screening and brief interventions for excessive alcohol use in primary health care. The majority of primary health care providers agree that the excessive consumption of alcohol is an important health issue and express their support to policies for reducing the impact of alcohol on the health of their patients. Notwithstanding, implementation of screening and brief interventions is low at the primary health care level. Therefore, the overall aim of this thesis is to investigate how to implement screening and brief interventions for excessive alcohol consumption in primary health care. This thesis reviewed the barriers of, and facilitators for, the implementation of alcohol screening and brief interventions in primary health care. Behaviour change theory was used to understand how these factors linked to the determinants of behaviour change and how they could be addressed in order to change primary health care providers’ behaviour, i.e. to increase the delivery of alcohol screening and brief interventions. A comprehensive theory-based implementation programme was designed and tested in a cluster randomized controlled trial. This thesis identified several barriers to implementation which were mapped to all the theoretical domains of behaviour change. Primary health care providers concerns about their ability to deliver alcohol screening and brief interventions and to help patients to cut down, lack of alcohol-related knowledge, lack of time, lack of materials and support, and providers’ attitudes towards at-risk drinkers were among the most commonly cited barriers. This thesis found evidence that the attitudes of family physicians could be used to divide practitioners into two distinct groups, one with more positive and the other with more negative attitudes towards at-risk drinkers. This thesis also found that a behaviour change theory-based programme, tailored to the barriers for, and facilitators of, the implementation of screening and brief intervention in primary health care is effective in increasing alcohol screening rates. This thesis contributed to the evidence base by providing researchers with practical evidence on how to address the factors influencing the implementation of screening and brief interventions in primary health care. This thesis also provides researchers with insight into the behavioural mechanisms mediating primary health care providers’ decision to deliver alcohol screening and brief interventions. The results of this thesis could be used by researchers and policymakers to inform the design of novel theory-oriented interventions to support the implementation of alcohol screening and brief interventions in primary health care

    Web-based platform to collect, share and manage technical data of historical systemic architectures: the Telegraphic Towers along the Madrid-Valencia path

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    Considering the variety of architectural Cultural Heritage typologies, systemic architectures require specific attention in the recovery process. The dimensions of "extension" and "recurrence" at geographic and technological levels affect the complexity of their knowledge process; they require systematic ways for their categorisation and comprehension to guarantee correct diagnosis and suitable rehabilitation. Recent applications involving Internet of Things (IoT) for the built Cultural Heritage have demonstrated the potentialities of three-dimensional (3D) geographic information system (GIS) models and structured databases in supporting complex degrees of knowledge for technicians, as well as management for administrators. Starting from such experiences, the work presents the setting up of a web-based platform to support the knowledge and management of systemic architectures, considering the geographical distribution of fabrics, natural and anthropic boundary conditions, and technical and administrative details. The platform takes advantage of digital models, machine and deep learning procedures and relational databases, in a GIS-based environment, for the recognition and categorisation of prevalent physical and qualitative features of systemic architectures, the recognition and qualification of dominant and recurrent decays and the management of recovery activities in a semi-automatic way. Specifically, the main digital objects used for testing the applied techniques and setting up the platform are based on Red-Green-Blue (RGB) and mapped point clouds of the historical Telegraphic Towers located along the Madrid-Valencia path, resulting from the on-site investigations. Their choice is motivated by the high level of knowledge about the cases reached in the last years by the authors, allowing them to test rules within the decision support systems and innovative techniques for their decay mapping. As the experience has demonstrated, the systematisation of technical details and operative pipeline of methods and tools allow the normalisation and standardisation of the intervention selection process; this offers policymakers an innovative tool based on traditional procedures for conservation plans, coherent with a priority-based practice

    Automatic Calibration and Error Correction for Large Language Models via Pareto Optimal Self-Supervision

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    Large language models (LLMs) have demonstrated remarkable capabilities out of box for a wide range of applications, yet accuracy still remains a major growth area, especially in mission-critical domains such as biomedicine. An effective method to calibrate the confidence level on LLM responses is essential to automatically detect errors and facilitate human-in-the-loop verification. An important source of calibration signals stems from expert-stipulated programmatic supervision, which is often available at low cost but has its own limitations such as noise and coverage. In this paper, we introduce a Pareto optimal self-supervision framework that can leverage available programmatic supervision to systematically calibrate LLM responses by producing a risk score for every response, without any additional manual efforts. This is accomplished by learning a harmonizer model to align LLM output with other available supervision sources, which would assign higher risk scores to more uncertain LLM responses and facilitate error correction. Experiments on standard relation extraction tasks in biomedical and general domains demonstrate the promise of this approach, with our proposed risk scores highly correlated with the real error rate of LLMs. For the most uncertain test instances, dynamic prompting based on our proposed risk scores results in significant accuracy improvement for off-the-shelf LLMs, boosting GPT-3 results past state-of-the-art (SOTA) weak supervision and GPT-4 results past SOTA supervised results on challenging evaluation datasets

    Novel Representations of Semialgebraic Sets Arising in Planning and Control

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    The mathematical notion of a set arises frequently in planning and control of autonomous systems. A common challenge is how to best represent a given set in a manner that is efficient, accurate, and amenable to computational tools of interest. For example, ensuring a vehicle does not collide with an obstacle can be generically posed in multiple ways using techniques from optimization or computational geometry. However these representations generally rely on executing algorithms instead of evaluating closed-form expressions. This presents an issue when we wish to represent an obstacle avoidance condition within a larger motion planning problem which is solved using nonlinear optimization. These tools generally can only accept smooth, closed-form expressions. As such our available representations of obstacle avoidance conditions, while accurate, are not amenable to the relevant tools. A related problem is how to represent a set in a compact form without sacrificing accuracy. For example, we may be presented with point-cloud data representing the boundary of an object that our vehicle must avoid. Using the obstacle avoidance conditions directly on the point-cloud data would require performing these calculations with respect to each point individually. A more efficient approach is to first approximate the data with simple geometric shapes and perform later analysis with the approximation. Common shapes include bounding boxes, ellipsoids, and superquadrics. These shapes are convenient in that they have a compact representation and we have good heuristic objectives for fitting the data. However, their primitive nature means accuracy of representation may suffer. Most notably, their inherent symmetry makes them ill-suited for representing asymmetric shapes. In theory we could consider more complicated shapes given by an implicit function. However we lack reliable methods for ensuring a good fit. This thesis proposes novel approaches to these problems based on tools from convex optimization and convex analysis. Throughout, the sets of interest are described by polynomial inequalities, making them semialgebraic

    Relightable and Animatable Neural Avatar from Sparse-View Video

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    This paper tackles the challenge of creating relightable and animatable neural avatars from sparse-view (or even monocular) videos of dynamic humans under unknown illumination. Compared to studio environments, this setting is more practical and accessible but poses an extremely challenging ill-posed problem. Previous neural human reconstruction methods are able to reconstruct animatable avatars from sparse views using deformed Signed Distance Fields (SDF) but cannot recover material parameters for relighting. While differentiable inverse rendering-based methods have succeeded in material recovery of static objects, it is not straightforward to extend them to dynamic humans as it is computationally intensive to compute pixel-surface intersection and light visibility on deformed SDFs for inverse rendering. To solve this challenge, we propose a Hierarchical Distance Query (HDQ) algorithm to approximate the world space distances under arbitrary human poses. Specifically, we estimate coarse distances based on a parametric human model and compute fine distances by exploiting the local deformation invariance of SDF. Based on the HDQ algorithm, we leverage sphere tracing to efficiently estimate the surface intersection and light visibility. This allows us to develop the first system to recover animatable and relightable neural avatars from sparse view (or monocular) inputs. Experiments demonstrate that our approach is able to produce superior results compared to state-of-the-art methods. Our code will be released for reproducibility.Comment: Project page: https://zju3dv.github.io/relightable_avata

    Decision-making with gaussian processes: sampling strategies and monte carlo methods

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    We study Gaussian processes and their application to decision-making in the real world. We begin by reviewing the foundations of Bayesian decision theory and show how these ideas give rise to methods such as Bayesian optimization. We investigate practical techniques for carrying out these strategies, with an emphasis on estimating and maximizing acquisition functions. Finally, we introduce pathwise approaches to conditioning Gaussian processes and demonstrate key benefits for representing random variables in this manner.Open Acces

    Tradition and Innovation in Construction Project Management

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    This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

    HexPlane: A Fast Representation for Dynamic Scenes

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    Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than 100×100\times. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlanes are a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.Comment: Project page: https://caoang327.github.io/HexPlan

    Constitutions of Value

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    Gathering an interdisciplinary range of cutting-edge scholars, this book addresses legal constitutions of value. Global value production and transnational value practices that rely on exploitation and extraction have left us with toxic commons and a damaged planet. Against this situation, the book examines law’s fundamental role in institutions of value production and valuation. Utilising pathbreaking theoretical approaches, it problematizes mainstream efforts to redeem institutions of value production by recoupling them with progressive values. Aiming beyond radical critique, the book opens up the possibility of imagining and enacting new and different value practices. This wide-ranging and accessible book will appeal to international lawyers, socio-legal scholars, those working at the intersections of law and economy and others, in politics, economics, environmental studies and elsewhere, who are concerned with rethinking our current ideas of what has value, what does not, and whether and how value may be revalued
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