19200 research outputs found
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Carice Van Houten / Climate Action For Everyone / Climate Majority Project
No full textShort Campaign Film 1x60” – writer/directorShort climate action film with Carice Van Houten (Game Of Thrones) alongside Professor Rupert Read’s brief for the Climate Majority Project NGO. It reached her 1.1m followers with 16K Likes on Instagram alone. https://climatemajorityproject.com/<br/
‘This is what happens to people who don't spank their kids’: An analysis of YouTube comments to news reports of child to parent violence
No full textChild to parent violence (CPV) is a form of family violence that has seen a growth in research attention over the past decade. However, little research has examined how this problem is understood by those outside of academia. This is despite recognition that public understandings of a particular social problem shape the landscape in which that problem plays out. To address this research gap, we analysed data from 463 public comments posted on YouTube in response to four UK news broadcasts that reported on the problem. Using a discourse analytic approach, we examined how posted comments framed the cause of, and solutions to, CPV. While a range of causes were identified, the posts predominantly blamed the parents, mobilizing child development discourses to attribute the cause of CPV to either parental use of violence, or to a lack of parental violence, towards children. The solutions offered reflect a penal populism that supports extremely punitive sanctions against children who engage in CPV. This study suggests that public campaign and education initiatives need to do more than raise public awareness about CPV—they must also inform the public about how broader social policy operates in relation to children, families and the state
Exploring the experiences of Muslim doctoral students in UK higher education: challenges, implications, and pathways to inclusivity
No full textGrowing Up As A Third Culture Kid and Its Impact On Psychological Well-Being and Interpersonal Relatedness
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Using patient preferences to customise therapy
No full textOver the past decade, research has significantly advanced an understanding of how client preferences in psychotherapy impact treatment efficacy. Studies have indicated that assessing and accommodating preferences can lead to improved therapeutic outcomes, such as lower dropout rates, enhanced therapeutic alliances and greater psychological well-being. Meta-analyses have supported the notion that personalised psychotherapy tailored to client preferences is effective. This approach is also considered an ethical imperative, addressing patients’ legitimate needs and contributing to their overall well-being. Understanding and integrating client preferences into therapy aligns with the broader movement towards personalised medicine. This chapter explores the significance of patient preferences, detailing the benefits and challenges. Preferences in psychotherapy encompass various aspects, including therapeutic approaches, therapist characteristics, session frequency and duration and specific treatment goals. Recognising these preferences can foster a stronger therapeutic alliance because clients feel acknowledged and respected, leading to better engagement and outcomes. This chapter highlights several instruments for assessing client preferences, and then focuses on a reliable and clinically useful measure, ‘Cooper-Norcross Inventory of Preferences’ (C-NIP), which has been developed in 2016 and addresses several limitations of previously developed preference tools. The C-NIP has been translated into multiple languages, making it widely applicable. This chapter also offers guidelines for using the C-NIP in therapy, with clinical examples. Integrating patient preferences into therapy involves adopting, adapting or offering alternatives to meet client needs while adhering to ethical and evidence-based practices. This process enhances the therapeutic alliance, promotes patient autonomy and ensures that therapy is both effective and personalised. By embracing shared decision-making, therapists can collaborate with clients to tailor treatments that respect their preferences and contribute to their overall well-being
Markowitz vs. 1/N: Portfolio Performance, Estimation Errors, and Subjectivity
No full textThis paper reconciles the ‘mean-variance (MV) vs equal-weighting (1/N)’ performance debate using an unbiased performance-to-error map and delineates thresholds where subjective interventions become essential to surpass MV optimisation. Traditional evaluations often yield inconclusive or contradictory results due to a ‘joint-test’ problem entangled with alpha and risk models. We introduce a methodology that isolates portfolio selection, through which we objectively assess performance sensitivity to estimation errors, identify when MV becomes suboptimal, and explore why appropriate subjectivity can enhance performance beyond MV. These findings underscore the importance of structurally incorporating subjectivity and rational investor behaviours into portfolio theory and practice
Enhancing Heart Health Prediction with Natural Remedies Through Integration of Hybrid Deep Learning Models
No full textCardiovascular disease remains a leading cause of mortality worldwide, necessitating advanced predictive models to improve early detection and prevention. The integration of natural remedies with machine learning techniques offers a promising approach for enhancing heart disease prediction. Aim: This study aims to develop a hybrid learning model for predicting cardiac disease by combining machine learning algorithms with natural remedies to improve the model’s accuracy and clinical applicability. Methods: A dataset titled “Indicators of Heart Disease (2022 UPDATE)” containing 246,023 patient records was sourced from Kaggle. The hybrid model combines Random Forest (RF) for interpretability and Long Short-Term Memory (LSTM) networks for time-series analysis. Features related to herbal medicines and their impact on heart health were incorporated to enhance predictive accuracy. Results: The hybrid model achieved an accuracy of 100%, demonstrating the potential of integrating traditional medical data with natural remedies to enhance cardiovascular disease forecasting. The inclusion of natural remedies provided a comprehensive tool for clinicians, enabling more precise decision-making. Conclusion: Integrating natural remedies into machine learning models is a promising direction for improving the prediction and early prevention of heart disease. This approach offers a sustainable and accessible solution to cardiovascular healthcare, with the potential to significantly improve patient outcomes