4,225 research outputs found

    Exploring the use of nature as an adjunct to psychological interventions for depression in young populations

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    Depression in adolescence is a global priority and it is critical to identify effective and accessible interventions. This systematic review aimed to synthesise experimental research on nature-based interventions (NBIs), to determine effects on depressive symptoms in young people. The secondary research question sought to understand characteristics of effective NBIs. A comprehensive systematic search was conducted across major and grey literature databases and papers were screened according to specified criteria. Participants’ ages were required to be between 10 and 24 years and studies needed to use an experimental design, including a control group. Experimental conditions were defined by psychotherapeutic interventions with nature exposure and outcomes measured either clinical symptomatology or subjective states of depression. Ten papers were identified, quality assessed and summarised in a narrative synthesis. Thirteen significant effects were reported in nine studies, highlighting the potential for NBIs as effective interventions for depressive symptoms in young people. However, due to methodological biases, such as lack of randomisation or control over group differences and frequent use of passive control groups, there remains considerable uncertainty over the effectiveness of NBIs. Characteristics of effective NBIs are tentatively discussed, however, further work is needed to clarify which aspects specifically contribute to the beneficial effects observed. Future research should seek to address the limitations of small samples, selection biases and test NBIs against more comparable and evidence-based interventions. It is hoped future studies will consider the inclusion of clinical populations, to explore the utility of NBIs as a treatment option for adolescent depression

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    NURSING AND MIDWIFERY STUDENTS’ LENS: CONNECTING THEORETICAL KNOWLEDGE WITH CLINICAL PRACTICE: AN INTERPRETATIVE PHENOMENOLOGICAL STUDY

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    Aim: To explore and critically analyse the strategies employed by final-year BSc pre-registration nursing and midwifery students at an inner London university to connect theoretical knowledge with clinical practice, to promote their learning and professional development. Background: Navigating the theory-practice gap has been a significant challenge for nursing and midwifery students. While there are many perspectives from academics and clinicians, how theoretical knowledge is connected with clinical practice is rarely discussed and studied from the students’ perspectives. Design: Interpretative phenomenological analysis was used to understand nursing and midwifery students' experiences in connecting theoretical knowledge with clinical practice. Rather than attempting to establish objective truth, this thesis focused on participants’ subjective experiences. Method: This study employed a qualitative research design. The data was obtained using semi-structured interviews and analysed using an inductive approach. The study population included (n=12) pre-registration nursing and midwifery students enrolled on a Bachelor of Science programs. Findings: Four themes emerged (1) Complexity of embodied knowledge; (2) Sensing the meaning of personal and professional learning; (3) Demographic attributes and self-understanding; (4) Sense-making of COVID-19. Conclusion: The process by which pre-registration nursing and midwifery students connect theoretical knowledge with clinical practice is complex and multifaceted. It intersects with other factors and cannot be understood in isolation. This interconnectedness necessitates a thorough examination of all the variables involved

    Security Aspects in Web of Data Based on Trust Principles. A brief of Literature Review

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    Within scientific community, there is a certain consensus to define "Big Data" as a global set, through a complex integration that embraces several dimensions from using of research data, Open Data, Linked Data, Social Network Data, etc. These data are scattered in different sources, which suppose a mix that respond to diverse philosophies, great diversity of structures, different denominations, etc. Its management faces great technological and methodological challenges: The discovery and selection of data, its extraction and final processing, preservation, visualization, access possibility, greater or lesser structuring, between other aspects, which allow showing a huge domain of study at the level of analysis and implementation in different knowledge domains. However, given the data availability and its possible opening: What problems do the data opening face? This paper shows a literature review about these security aspects

    Unveiling the frontiers of deep learning: innovations shaping diverse domains

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    Deep learning (DL) enables the development of computer models that are capable of learning, visualizing, optimizing, refining, and predicting data. In recent years, DL has been applied in a range of fields, including audio-visual data processing, agriculture, transportation prediction, natural language, biomedicine, disaster management, bioinformatics, drug design, genomics, face recognition, and ecology. To explore the current state of deep learning, it is necessary to investigate the latest developments and applications of deep learning in these disciplines. However, the literature is lacking in exploring the applications of deep learning in all potential sectors. This paper thus extensively investigates the potential applications of deep learning across all major fields of study as well as the associated benefits and challenges. As evidenced in the literature, DL exhibits accuracy in prediction and analysis, makes it a powerful computational tool, and has the ability to articulate itself and optimize, making it effective in processing data with no prior training. Given its independence from training data, deep learning necessitates massive amounts of data for effective analysis and processing, much like data volume. To handle the challenge of compiling huge amounts of medical, scientific, healthcare, and environmental data for use in deep learning, gated architectures like LSTMs and GRUs can be utilized. For multimodal learning, shared neurons in the neural network for all activities and specialized neurons for particular tasks are necessary.Comment: 64 pages, 3 figures, 3 table

    Trusted Provenance with Blockchain - A Blockchain-based Provenance Tracking System for Virtual Aircraft Component Manufacturing

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    The importance of provenance in the digital age has led to significant interest in utilizing blockchain technology for tamper-proof storage of provenance data. This thesis proposes a blockchain-based provenance tracking system for the certification of aircraft components. The aim is to design and implement a system that can ensure the trustworthy, tamper-resistant storage of provenance documents originating from an aircraft manufacturing process. To achieve this, the thesis presents a systematic literature review, which provides a comprehensive overview of existing works in the field of provenance and blockchain technology. After obtaining strategies to utilize blockchain for the storage of provenance data on the blockchain, a system was designed to meet the requirements of stakeholders in the aviation industry. The thesis utilized a systematic approach to gather requirements by conducting interviews with stakeholders. The system was implemented using a combination of smart contracts and a graphical user interface to provide tamper-resistant, traceable storage of relevant data on a transparent blockchain. An evaluation based on the requirements identified during the requirement engineering process found that the proposed system meets all identified requirements. Overall, this thesis offers insight into a potential application of blockchain technology in the aviation industry and provides a valuable resource for researchers and industry professionals seeking to leverage blockchain technology for provenance tracking and certification purpose

    Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts

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    Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support
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