2,797 research outputs found
A tailored psychological intervention for anxiety and depression management in people with chronic obstructive pulmonary disease: TANDEM RCT and process evaluation
Background: People with chronic obstructive pulmonary disease have high levels of anxiety and depression, which is associated with increased morbidity and poor uptake of effective treatments, such as pulmonary rehabilitation. Cognitive-behavioural therapy improves mental health of people with long-term conditions and could potentially increase uptake of pulmonary rehabilitation, enabling synergies that could enhance the mental health of people with chronic obstructive pulmonary disease. Aim: Our aim was to develop and evaluate the clinical effectiveness and cost effectiveness of a tailored cognitive-behavioural approach intervention, which links into, and optimises the benefits of, routine pulmonary rehabilitation. Design: We carried out a pragmatic multicentre randomised controlled trial using a 1.25 : 1 ratio (intervention : control) with a parallel process evaluation, including assessment of fidelity. Setting: Twelve NHS trusts and five Clinical Commissioning Groups in England were recruited into the study. The intervention was delivered in participant\u27s own home or at a local NHS facility, and by telephone. Participants: Between July 2017 and March 2020 we recruited adults with moderate/very severe chronic obstructive pulmonary disease and mild/moderate anxiety and/or depression, meeting eligibility criteria for assessment for pulmonary rehabilitation. Carers of participants were invited to participate. Intervention: The cognitive-behavioural approach intervention (i.e. six to eight 40- to 60-minute sessions plus telephone support throughout pulmonary rehabilitation) was delivered by 31 trained respiratory healthcare professionals to participants prior to commencing pulmonary rehabilitation. Usual care included routine pulmonary rehabilitation referral. Main outcome measures: Co-primary outcomes were Hospital Anxiety and Depression Scale - anxiety and Hospital Anxiety and Depression Scale - depression at 6 months post randomisation. Secondary outcomes at 6 and 12 months included health-related quality of life, smoking status, uptake of pulmonary rehabilitation and healthcare use. Results: We analysed results from 423 randomised participants (intervention, n = 242; control, n = 181). Forty-three carers participated. Follow-up at 6 and 12 months was 93% and 82%, respectively. Despite good fidelity for intervention delivery, mean between-group differences in Hospital Anxiety and Depression Scale at 6 months ruled out clinically important effects (Hospital Anxiety and Depression Scale - anxiety mean difference -0.60, 95% confidence interval -1.40 to 0.21; Hospital Anxiety and Depression Scale - depression mean difference -0.66, 95% confidence interval -1.39 to 0.07), with similar results at 12 months. There were no between-group differences in any of the secondary outcomes. Sensitivity analyses did not alter these conclusions. More adverse events were reported for intervention participants than for control participants, but none related to the trial. The intervention did not generate quality-of-life improvements to justify the additional cost (adjusted mean difference \ua3770.24, 95% confidence interval -\ua327.91 to \ua31568.39) to the NHS. The intervention was well received and many participants described positive affects on their quality of life. Facilitators highlighted the complexity of participants\u27 lives and considered the intervention to be of potential valuable; however, the intervention would be difficult to integrate within routine clinical services. Our well-powered trial delivered a theoretically designed intervention with good fidelity. The respiratory-experienced facilitators were trained to deliver a low-intensity cognitive-behavioural approach intervention, but high-intensity cognitive-behavioural therapy might have been more effective. Our broad inclusion criteria specified objectively assessed anxiety and/or depression, but participants were likely to favour talking therapies. Randomisation was concealed and blinding of outcome assessment was breached in only 15 participants. Conclusions: The tailored cognitive-behavioural approach intervention delivered with fidelity by trained respiratory healthcare professionals to people with chronic obstructive pulmonary disease was neither clinically effective nor cost-effective. Alternative approaches that are integrated with routine long-term condition care are needed to address the unmet, complex clinical and psychosocial needs of this group of patients. Trial registration: This trial is registered as ISRCTN59537391. Funding: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 13/146/02) and is published in full in Health Technology Assessment; Vol. 28, No. 1. See the NIHR Funding and Awards website for further award information.People with long-standing lung problems, such as chronic obstructive pulmonary disease, often also have anxiety and depression, which further reduces their quality of life. Two existing treatments could help. Pulmonary rehabilitation (a programme of exercise and education) improves both the physical and mental health of people with chronic obstructive pulmonary disease. Cognitiveâbehavioural therapy (a talking therapy) may reduce anxiety and depression. The TANDEM [Tailored intervention for Anxiety and Depression Management in chronic obstructive pulmonary disease (COPD)] intervention linked these two treatments by providing talking therapy based on cognitiveâbehavioural therapy during the waiting time following referral for pulmonary rehabilitation. The TANDEM treatment was delivered by respiratory healthcare professionals (e.g. nurses or physiotherapists) trained to deliver the talking therapy in six to eight weekly sessions. The sessions were conducted in the participantâs home (or another convenient location), with brief telephone support during the pulmonary rehabilitation. Of 423 participants recruited to the study, 242 participants received TANDEM talking therapy and 181 participants received usual care (including a referral to pulmonary rehabilitation). We measured mental health, quality of life, social life, attendance at pulmonary rehabilitation and healthcare use in both groups at 6 and 12 months. Forty-three carers joined the study and we assessed their mental well-being. We interviewed patients, carers and health professionals to find out their views and experience of the TANDEM treatment. We also examined whether or not the TANDEM treatment was good value for money. The TANDEM treatment did not improve the mental or the physical health of people with chronic obstructive pulmonary disease. In addition, the TANDEM treatment cost the NHS an extra \ua3770 per patient, which was not good value for money. The TANDEM treatment was well received, and many participants told us how it had helped them. Heath-care professionals noted how participants did not just have chronic obstructive pulmonary disease, but were coping with many physical, mental and social problems. The TANDEM intervention was not effective and, therefore, other strategies will be needed to help people with chronic obstructive pulmonary disease and mental health problems live with their condition
Separately, Connectedly: Exploring Trauma Through Ekphrasis in Contemporary Novels
This thesis examines ekphrasis as a rhetorical tool to explore, represent, and contemplate trauma affect in contemporary novels. From the Greek phrase for âdescription,â ekphrasis is part of a long and ancient literary tradition, dating as far back as the ancient depictions of art on urns, weaponry, as well as more disambiguated descriptions of scenes and people. The uses of ekphrasis as a literary device are broad and complex, but its use is under-researched in contemporary novels, and there is a near total absence of investigation into ekphrasis within the novel as a means of contemplating and understanding the affect of a condition that is inherently abstract and disorienting.Literary trauma theory has evolved considerably in recent years. In keeping with important findings in psychology and psychiatric research, there is a broad recognition that rethinking trauma representation beyond the recitation and reliving of events and into textured descriptions of trauma affect is essential for thoughtful, nuanced explorations of an experience that resists narrative convenience. As a result, there are increased calls to accept and represent its inherent fractured nature and resist the authorial temptation to forge a story around it that fits neatly into a cohesive whole. This thesis proposes a framework for considering how various aspects of ekphrastic descriptions of real and imagined art as well as their connotative and denotative significance in the novel reveals nuance in the representation of trauma affect through the activation of language and image. The contemporary novels explored herein are: The Goldfinch by Donna Tartt, What I Loved by Siri Hustvedt, and How to Be Both by Ali Smith. Each of these novels present ekphrasis and affect differently, which enables broader testing of the flexibility of the proposed framework
"It's not a career": Platform work among young people aged 16-19
In the online gig economy, or platform work as it is sometimes known, work can be organised through websites and smartphone apps. People can drive for Uber or Deliveroo, sell items on eBay or Etsy, or rent their properties on Airbnb.
This research examines the views of young people between the ages of 16 and 19 in the United Kingdom to see whether they knew about the online gig economy, whether they were using it already to earn money, and whether they expected to use it for their careers. It discovers careers professionalsâ levels of knowledge, and their ability (and desire) to include the gig economy in their professional practice.
This research contributes to discussions about what constitutes decent work, and whether it can be found within the online gig economy. The results point to ways in which careers practice could include platform work as a means of extending young peopleâs knowledge about alternative forms of work. This study also makes a theoretical contribution to literature, bringing together elements of careership, cognitive schema theory, and motivational theory and psychology of working theory, in a novel combination, to explain how young people were thinking about platform work in the context of their careers
âSo what if ChatGPT wrote it?â Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPTâs capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPTâs use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts
Introduction to Psychology
Introduction to Psychology is a modified version of Psychology 2e - OpenStax
Runway Safety Improvements Through a Data Driven Approach for Risk Flight Prediction and Simulation
Runway overrun is one of the most frequently occurring flight accident types threatening the safety of aviation. Sensors have been improved with recent technological advancements and allow data collection during flights. The recorded data helps to better identify the characteristics of runway overruns. The improved technological capabilities and the growing air traffic led to increased momentum for reducing flight risk using artificial intelligence. Discussions on incorporating artificial intelligence to enhance flight safety are timely and critical. Using artificial intelligence, we may be able to develop the tools we need to better identify runway overrun risk and increase awareness of runway overruns. This work seeks to increase attitude, skill, and knowledge (ASK) of runway overrun risks by predicting the flight states near touchdown and simulating the flight exposed to runway overrun precursors.
To achieve this, the methodology develops a prediction model and a simulation model. During the flight training process, the prediction model is used in flight to identify potential risks and the simulation model is used post-flight to review the flight behavior. The prediction model identifies potential risks by predicting flight parameters that best characterize the landing performance during the final approach phase. The predicted flight parameters are used to alert the pilots for any runway overrun precursors that may pose a threat. The predictions and alerts are made when thresholds of various flight parameters are exceeded. The flight simulation model simulates the final approach trajectory with an emphasis on capturing the effect wind has on the aircraft. The focus is on the wind since the wind is a relatively significant factor during the final approach; typically, the aircraft is stabilized during the final approach. The flight simulation is used to quickly assess the differences between fight patterns that have triggered overrun precursors and normal flights with no abnormalities. The differences are crucial in learning how to mitigate adverse flight conditions. Both of the models are created with neural network models. The main challenges of developing a neural network model are the unique assignment of each model design space and the size of a model design space. A model design space is unique to each problem and cannot accommodate multiple problems. A model design space can also be significantly large depending on the depth of the model. Therefore, a hyperparameter optimization algorithm is investigated and used to design the data and model structures to best characterize the aircraft behavior during the final approach.
A series of experiments are performed to observe how the model accuracy change with different data pre-processing methods for the prediction model and different neural network models for the simulation model. The data pre-processing methods include indexing the data by different frequencies, by different window sizes, and data clustering. The neural network models include simple Recurrent Neural Networks, Gated Recurrent Units, Long Short Term Memory, and Neural Network Autoregressive with Exogenous Input. Another series of experiments are performed to evaluate the robustness of these models to adverse wind and flare. This is because different wind conditions and flares represent controls that the models need to map to the predicted flight states. The most robust models are then used to identify significant features for the prediction model and the feasible control space for the simulation model. The outcomes of the most robust models are also mapped to the required landing distance metric so that the results of the prediction and simulation are easily read. Then, the methodology is demonstrated with a sample flight exposed to an overrun precursor, and high approach speed, to show how the models can potentially increase attitude, skill, and knowledge of runway overrun risk.
The main contribution of this work is on evaluating the accuracy and robustness of prediction and simulation models trained using Flight Operational Quality Assurance (FOQA) data. Unlike many studies that focused on optimizing the model structures to create the two models, this work optimized both data and model structures to ensure that the data well capture the dynamics of the aircraft it represents. To achieve this, this work introduced a hybrid genetic algorithm that combines the benefits of conventional and quantum-inspired genetic algorithms to quickly converge to an optimal configuration while exploring the design space. With the optimized model, this work identified the data features, from the final approach, with a higher contribution to predicting airspeed, vertical speed, and pitch angle near touchdown. The top contributing features are altitude, angle of attack, core rpm, and air speeds. For both the prediction and the simulation models, this study goes through the impact of various data preprocessing methods on the accuracy of the two models. The results may help future studies identify the right data preprocessing methods for their work. Another contribution from this work is on evaluating how flight control and wind affect both the prediction and the simulation models. This is achieved by mapping the model accuracy at various levels of control surface deflection, wind speeds, and wind direction change. The results saw fairly consistent prediction and simulation accuracy at different levels of control surface deflection and wind conditions. This showed that the neural network-based models are effective in creating robust prediction and simulation models of aircraft during the final approach. The results also showed that data frequency has a significant impact on the prediction and simulation accuracy so it is important to have sufficient data to train the models in the condition that the models will be used. The final contribution of this work is on demonstrating how the prediction and the simulation models can be used to increase awareness of runway overrun.Ph.D
A comparative analysis of gender equality law in Europe 2022
This report provides a general overview of the ways in which EU gender equality law has been implemented in the domestic laws of the 27 Member States of the European Union, as well as Iceland, Liechtenstein and Norway (the EEA countries), the United Kingdom and five candidate countries (Albania, Montenegro, North Macedonia, Serbia and Turkey). The analysis is based on the country reports written by the gender equality law experts of the European equality law network (EELN). At the same time, the report explains the most important elements of the EU gender equality acquis. The term âEU gender equality acquisâ refers to all the relevant EU Treaty and EU Charter of Fundamental Rights provisions, legislation and case law of the CJEU in relation to gender equality
Hybrid human-AI driven open personalized education
Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer.
In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer).
All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result
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Exploring the socioeconomic and environmental factors influencing smallholder macadamia production and productivity in Malawi.
Macadamia (Macadamia integrifolia Maiden & Betche) is a highly valued crop in Malawi. The crop is a vital source of food security and ecosystem services, and its high-export cash value makes it a key contributor to the country's economy. Malawi ranks seventh in global macadamia production, comprising two subsectors: smallholders and commercial estates. However, significant yield gaps have been reported between smallholder and commercial estate producers. While commercial estates achieve higher average annual tree yields (30 kg), smallholder yields remain consistently low, averaging at or below 10 kg tree-1 year-1. Improving macadamia productivity among smallholders can help reduce poverty, improve household food security, and promote economic growth in Malawi.
Despite the significant contributions of smallholders in the Malawian macadamia subsector, research on the factors influencing the crop's productivity has primarily focused on commercial estate production. To address this knowledge gap, this Ph.D thesis focuses on smallholder macadamia production in Malawi. The thesis examines the socioeconomic characteristics of smallholder macadamia farmers, including demographics, cultivar preferences, and production constraints. Secondly, it evaluates the climatic factors influencing smallholder macadamia production and predicts the current and future suitable geographical areas for the crop. Lastly, it assesses the soil fertility status of smallholder macadamia farms in relation to macadamia production requirements.
Results of this study reveal that the majority (62%) of macadamia smallholders are over 50 years of age and consider farming their main occupation. However, this poses significant risks to the macadamia subsector, as older farmers are risk-averse and less innovative, hindering their willingness to adopt new agricultural technologies and ability to learn. Regarding cultivar preferences, the study finds that smallholder macadamia farmers prefer high-yielding cultivars with superior nut qualities, such as large and heavy nuts, and extended flowering periods. The most preferred macadamia cultivars in descending order are Hawaiian Agricultural Experimental Station (HAES) 660, 800, 816, and 246, which are the "core" of established cultivars in Malawi. The study identifies insect pests, diseases, market availability, strong winds, and a lack of agricultural extension services as the most significant challenges affecting smallholder macadamia farmers.
The study's suitability analysis reveals that the ensemble model has an excellent fit and high performance in predicting the current agro-climatically suitable areas for macadamia production (AUC = 0.90). The findings show that precipitation related variables (60.2%) are more important in determining the suitable areas for growing macadamia than temperature related variables (39.8%). The model results show that 57% (53,925 km2) of Malawi is currently suitable for macadamia cultivation, with the central region having the highest suitability (25.8%, 24,327 km2) and the southern region the lowest (10.7%, 10,257 km2). Optimal suitability (26%, 24,565 km2) is observed in the highland areas with elevations ranging from 1000â1400 metres above sea level (m.a.s.l.). Under the intermediate emission scenario (RCP 4.5) and the pessimistic scenario (RCP 8.5), the impact models predict net losses of 18% (17,015 km2) and 21.6% (20,414 km2), respectively, in the extent of suitable areas for macadamia in the 2050s.
The results of the soil fertility analysis indicate suboptimal fertility among the sampled macadamia farms. The majority of the soils are strongly acidic and deficient in essential nutrients required for the healthy growth of macadamia trees. Moreover, the average cation exchange capacity (1.67 cmol (+) kg-1) and the soil organic matter content (†1%) are below the minimum optimal levels required for macadamia trees. These findings indicate that soil fertility is one of the primary limiting factors to the crop's productivity, even in areas with suitable climatic conditions. Therefore, addressing the soil fertility issues is crucial to improving the land suitability of the smallholder farms for macadamia, which can lead to optimal yields.
This study extends the frontiers of knowledge concerning the macadamia subsector in Malawi by providing insights into the smallholder macadamia farming systems, including demographics, cultivar preferences, and production constraints. It also provides novel empirical evidence on the climate factors that influence the suitability of rainfed macadamia cultivation and identifies current and future suitable growing areas in the country. Additionally, the study addresses the research gap on the soil fertility status of Malawian smallholder macadamia farms. Therefore, the findings of this research have practical implications for various areas such as macadamia cultivar introductions and breeding, land use planning, soil fertility management, and policy formulation for agricultural extension services, inputs, and marketing of the crop
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