17,008 research outputs found

    Modifying Group Interpersonal Psychotherapy for Peripartum Adolescents in Sub-Saharan African Context: Reviewing Differential Contextual and Implementation Considerations

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    Background: This study describes adaptation and modification of World Health Organization (WHO) recommended group interpersonal psychotherapy (IPT-G) for depressed peripartum adolescents. The adaptation process includes accommodating contextual factors and strategies to address intervention implementation barriers, such as engagement problems with adolescents, caregivers, and providers, and stigma and dearth of mental health specialists. The modifications include and adolescent relevant iterations to the therapy format and content. Methods: A multi-stakeholder led two-stage intervention adaptation and modification process integrating mixed qualitative methods were used with pregnant and parenting adolescents, their partners, and health care workers. In-depth interviews focusing on personal, relationship, social, and cultural barriers experienced by adolescents were carried out modeled on the Consolidated Framework for Implementation Research. Focus group discussions with depressed adolescents on their experiences, feedback from caregivers, partners, health workers inform focused modifications. An IPT expert committee of three practitioners, along with UNICEF adolescent officer, and mental health policy expert from Ministry of Health and representative community advisory body reviewed the adaptations and modifications made to the WHO IPT-G manual. Discussion: Integration of mental health needs of peripartum adolescents as demonstrated in the stakeholder engagement process, adaptation of key terms into locally relevant language, determination of number of sessions, and user-centric design modifications to digitize a brief version of group interpersonal psychotherapy are presented

    Incentivising research data sharing : a scoping review

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    Background: Numerous mechanisms exist to incentivise researchers to share their data. This scoping review aims to identify and summarise evidence of the efficacy of different interventions to promote open data practices and provide an overview of current research. Methods: This scoping review is based on data identified from Web of Science and LISTA, limited from 2016 to 2021. A total of 1128 papers were screened, with 38 items being included. Items were selected if they focused on designing or evaluating an intervention or presenting an initiative to incentivise sharing. Items comprised a mixture of research papers, opinion pieces and descriptive articles. Results: Seven major themes in the literature were identified: publisher/journal data sharing policies, metrics, software solutions, research data sharing agreements in general, open science ‘badges’, funder mandates, and initiatives. Conclusions: A number of key messages for data sharing include: the need to build on existing cultures and practices, meeting people where they are and tailoring interventions to support them; the importance of publicising and explaining the policy/service widely; the need to have disciplinary data champions to model good practice and drive cultural change; the requirement to resource interventions properly; and the imperative to provide robust technical infrastructure and protocols, such as labelling of data sets, use of DOIs, data standards and use of data repositories

    Image classification over unknown and anomalous domains

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    A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting. Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each. While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so. In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks

    Data-to-text generation with neural planning

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    In this thesis, we consider the task of data-to-text generation, which takes non-linguistic structures as input and produces textual output. The inputs can take the form of database tables, spreadsheets, charts, and so on. The main application of data-to-text generation is to present information in a textual format which makes it accessible to a layperson who may otherwise find it problematic to understand numerical figures. The task can also automate routine document generation jobs, thus improving human efficiency. We focus on generating long-form text, i.e., documents with multiple paragraphs. Recent approaches to data-to-text generation have adopted the very successful encoder-decoder architecture or its variants. These models generate fluent (but often imprecise) text and perform quite poorly at selecting appropriate content and ordering it coherently. This thesis focuses on overcoming these issues by integrating content planning with neural models. We hypothesize data-to-text generation will benefit from explicit planning, which manifests itself in (a) micro planning, (b) latent entity planning, and (c) macro planning. Throughout this thesis, we assume the input to our generator are tables (with records) in the sports domain. And the output are summaries describing what happened in the game (e.g., who won/lost, ..., scored, etc.). We first describe our work on integrating fine-grained or micro plans with data-to-text generation. As part of this, we generate a micro plan highlighting which records should be mentioned and in which order, and then generate the document while taking the micro plan into account. We then show how data-to-text generation can benefit from higher level latent entity planning. Here, we make use of entity-specific representations which are dynam ically updated. The text is generated conditioned on entity representations and the records corresponding to the entities by using hierarchical attention at each time step. We then combine planning with the high level organization of entities, events, and their interactions. Such coarse-grained macro plans are learnt from data and given as input to the generator. Finally, we present work on making macro plans latent while incrementally generating a document paragraph by paragraph. We infer latent plans sequentially with a structured variational model while interleaving the steps of planning and generation. Text is generated by conditioning on previous variational decisions and previously generated text. Overall our results show that planning makes data-to-text generation more interpretable, improves the factuality and coherence of the generated documents and re duces redundancy in the output document

    Structure and adsorption properties of gas-ionic liquid interfaces

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    Supported ionic liquids are a diverse class of materials that have been considered as a promising approach to design new surface properties within solids for gas adsorption and separation applications. In these materials, the surface morphology and composition of a porous solid are modified by depositing ionic liquid. The resulting materials exhibit a unique combination of structural and gas adsorption properties arising from both components, the support, and the liquid. Naturally, theoretical and experimental studies devoted to understanding the underlying principles of exhibited interfacial properties have been an intense area of research. However, a complete understanding of the interplay between interfacial gas-liquid and liquid-solid interactions as well as molecular details of these processes remains elusive. The proposed problem is challenging and in this thesis, it is approached from two different perspectives applying computational and experimental techniques. In particular, molecular dynamics simulations are used to model gas adsorption in films of ionic liquids on a molecular level. A detailed description of the modeled systems is possible if the interfacial and bulk properties of ionic liquid films are separated. In this study, we use a unique method that recognizes the interfacial and bulk structures of ionic liquids and distinguishes gas adsorption from gas solubility. By combining classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous support. The developed approach was applied to a range of ionic liquids that feature different interaction behavior with gas and porous support. Using molecular simulations with interfacial analysis, it was discovered that gas adsorption capacity can be directly related to gas solubility data, allowing the development of a predictive model for the gas adsorption performance of ionic liquid films. Furthermore, it was found that this CO2 adsorption on the surface of ionic liquid films is determined by the specific arrangement of cations and anions on the surface. A particularly important result is that, for the first time, a quantitative relation between these structural and adsorption properties of different ionic liquid films has been established. This link between two types of properties determines design principles for supported ionic liquids. However, the proposed predictive model and design principles rely on the assumption that the ionic liquid is uniformly distributed on the surface of the porous support. To test how ionic liquids behave under confinement, nitrogen physisorption experiments were conducted for micro‐ and mesopore analysis of supported ionic liquid materials. In conjunction with mean-field density functional theory applied to the lattice gas and pore models, we revealed different scenarios for the pore-filling mechanism depending on the strength of the liquid-solid interactions. In this thesis, a combination of computational and experimental studies provides a framework for the characterization of complex interfacial gas-liquid and liquid-solid processes. It is shown that interfacial analysis is a powerful tool for studying molecular-level interactions between different phases. Finally, nitrogen sorption experiments were effectively used to obtain information on the structure of supported ionic liquids

    Innovation systems’ response to changes in the institutional impulse: Analysis of the evolution of the European energy innovation system from FP7 to H2020

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    This study addresses how the institutional impulse developed by the European Union influenced the evolution of the European energy innovation system. Considering the contributing role of innovation systems in the development of new knowledge and technology, it can be stated that the institutional impulse achieved by the European Union through the research framework programmes creates a network of relations between entities and projects. This enables the exchange of information and expertise, which is considered a key element for innovation development. Previous studies have attempted to determine whether institutional impulse is an essential element in understanding the efficiency of innovation systems and their related research policies. However, their investigations have yielded inconclusive results. Using the CORDIS database of the European Commission, this study aims to fill this gap by assessing the European energy innovation system for two periods (2007–2013 and 2014–2020) through two of its research funding programmes—FP7 and H2020—thereby contributing to the literature in the innovation systems field. Social network analysis has been conducted to examine how changes in the institutional impulse, reflected in the new objectives in the research funding programmes, are associated with changes in the structural and topological properties of the innovation systems’ underlying networks. The first contribution indicates that the innovation system responds to changes in the goals of funding programmes, as the taxonomy, topology, and structural properties of their underlying networks underwent modifications due to the newly proposed objectives. The second contribution shows that network properties (cohesion and centrality metrics) can explain the efficiency and effectiveness of innovation systems, drawing useful conclusions for policymakers and individual entities. This last contribution also has important policymaking implications, as it provides the basis for understanding how innovation policy goals can be achieved by changing the institutional impulse to direct the innovation system towards these objectives

    Attitudes towards Animals and Meat Consumption: The Role of Ideology and Individual Differences

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    Humans' relationships with non-human animals are complicated and complex. This thesis aims to address questions on how people think about animals' moral standing, how information about food animals' sentience is (mis)remembered, and how people evaluate laboratory-grown meat relative to traditional meat. The first empirical chapter of the thesis, Chapter 3, explores the question of whether higher human supremacy beliefs are associated with a greater perceived moral divide between animals of high and low status. Across two studies (N = 196 and N = 256), the findings suggest that people holding stronger human supremacy beliefs also perceive a greater moral divide between animals, which may serve as a legitimising strategy to preserve not only the existing human-animal hierarchy, but also greater hierarchical divides between other animals. The second set of studies, presented in Chapter 4 (N = 253 and N = 255), focuses on food animals specifically, investigating the ideologically motivated memory processes involved in the processing of objective information about these animals' sentience. Indeed, dominance-based ideologies were significant predictors for targeted memory errors for information on food animals' sentience, but not for information on their uses (e.g., in medical science), suggesting that differences in ideological attitudes interfere with the correct recall of sentience information for food animals. The final set of studies, presented in Chapter 5 (total N = 1,169), turns its focus to the psychological barriers to acceptance of laboratory-grown meat, which is structurally identical to traditionally farmed meat and presents solutions to the ethical, environmental, and public health issues associated with traditional animal agriculture. The three experiments consistently demonstrated that omnivores who were wearier about new food technologies evaluated clean meat more negatively than traditional meat. Experiment 3 further demonstrated that safety concerns, but not naturalness concerns, partly explained why those wearier of novel food technologies evaluated clean meat less positively. Taken together, the findings highlight the role of general concerns about the use of new food technology as a psychological barrier to clean meat acceptance. This thesis thus adds to the growing body of literature on human-animal intergroup relations, providing further evidence for the ways in which individual differences and ideology affect peoples' thinking about animals of different socio-cultural status, as well as attitudes towards meat substitutes

    The company she keeps : The social and interpersonal construction of girls same sex friendships

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    This thesis begins a critical analysis of girls' 'private' interpersonal and social relations as they are enacted within two school settings. It is the study of these marginal subordinated worlds productivity of forms of femininity which provides the main narrative of this project. I seek to understand these processes of (best) friendship construction through a feminist multi-disciplinary frame, drawing upon cultural studies, psychoanalysis and accounts of gender politics. I argue that the investments girls bring to their homosocial alliances and boundary drawing narry a psychological compulsion which is complexly connected to their own experiences within the mother/daughter bond as well as reflecting positively an immense social debt to the permissions girls have to be nurturant and ; negatively their own reproduction of oppressive exclusionary practices. Best friendship in particular gives girls therefore, the experience of 'monogamy' continuous of maternal/daughter identification, reminiscent of their positioning inside monopolistic forms of heterosexuality. But these subcultures also represent a subversive discontinuity to the public dominance of boys/teachers/adults in schools and to the ideologies and practices of heterosociality and heterosexuality. By taking seriously their transmission of the values of friendship in their chosen form of notes and diaries for example, I was able to access the means whereby they were able to resist their surveillance and control by those in power over them. I conclude by arguing that it is through a recognition of the valency of these indivisiblly positive and negative aspects to girls cultures that Equal Opportunities practitioners must begin if they are serious about their ambitions. Methods have to be made which enable girls to transfer their 'private' solidarities into the 'public' realm, which unquestionably demands contesting with them the causes and consequences of their implication in the divisions which also contaminate their lives and weaken them
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