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Through an examination of "In-Common Sites," conducted with young people to investigate their relationship with Mousehold Heath in Norwich, this article considers the possibilities of performance to not UK, only represent a spatially defined urban common but also to enact a commons. It is argued that performance is a generative social practice: the ecological potential of both performance and commoning practices lies in their shared commitments to participation, collaboration, and interaction among people and their environments. Starting from the sensory and affective encounters between body and environment, I reflect on contested and entangled acts of human and more-than-human interconnection.

This article explores initiatives to decolonise the curriculum via two specific disciplines, namely Economics and Politics, both of which have tended to marginalise the study of race, empire, and colonialism and whose canonical thinkers are overwhelming white. By providing the first comparative analysis of decolonising initiatives in these disciplines, this article: investigates the extent to which Economics and Politics curricula in UK universities have been 'decolonised'; explores the factors which affect support for or resistance to decolonisation; and analyses the extent to which these factors share common roots in both disciplines. Our comparative method allows us to shed light on drivers of resistance that affect all disciplines alike and those that are rooted within specific disciplines. Using an audit of UK undergraduate courses and a survey of academics, we show that neither Politics nor Economics can plausibly claim to have made much progress in decolonising curricula. However, more progress has been made in Politics, and Politics staff are more informed about and less hostile to decolonising initiatives than Economics staff. We locate one of the reasons for this difference in the epistemological and ideological idiosyncrasies of the dominant neoclassical paradigm in Economics. We therefore argue that initiatives to decolonise the curriculum must take into account potential discipline-specific obstacles.

BackgroundText-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention. ObjectiveThis systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention. MethodsA systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar. ResultsOverall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being. ConclusionsOur findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

Introduction Despite the evidence supporting the value of digital supports for enhancing youth mental health services, there is a lack of guidance on how best to engage with young people in coproduction processes during the design and evaluation of these technologies. User input is crucial in digital mental health, especially for disadvantaged, vulnerable and marginalised young people as they are often excluded from coproduction. A scoping review of international literature written in English will explore the coproduction processes with marginalised young people in digital mental health supports, from mental health promotion to targeted interventions. The review is guided by the research question: what are the most appropriate coproduction processes for engaging young people, especially marginalised young people, in the different stages of designing and evaluating digital mental health supports? The review aims to map and summarise the evidence, inform the overall research project and address the knowledge gaps.Methods and analysis The scoping review uses Arksey and O’Malley’s framework and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols Extension for Scoping Reviews. From 22–24 October 2023, PubMed, Scopus, EBSCO, ASSIA, Web of Science, Ovid MEDLINE, Cochrane database, Embase, Google Scholar, ProQuest, OAIster and BASE will be systematically searched. Papers from 2021 onwards with a range of study designs and evidence that illustrate engagement with marginalised young people (aged 16–25) in the design, implementation and evaluation of digital technologies for young people’s mental health will be considered for inclusion. At least two reviewers will screen full texts and chart data. The results of this review will be summarised quantitatively through numerical counts of included literature and qualitatively through a narrative synthesis.Ethics and dissemination Ethical approval is not required. Results will be disseminated through publications in peer-reviewed journals.Trial registration number This scoping review protocol has been registered with the Open Science Framework (https://osf.io/9xhgv).

Abstract This paper makes a case for digital mental health and provides insights into how digital technologies can enhance (but not replace) existing mental health services. We describe digital mental health by presenting a suite of digital technologies (from digital interventions to the application of artificial intelligence). We discuss the benefits of digital mental health, for example, a digital intervention can be an accessible stepping-stone to receiving support. The paper does, however, present less-discussed benefits with new concepts such as ‘poly-digital’, where many different apps/features (e.g. a sleep app, mood logging app and a mindfulness app, etc.) can each address different factors of wellbeing, perhaps resulting in an aggregation of marginal gains. Another benefit is that digital mental health offers the ability to collect high-resolution real-world client data and provide client monitoring outside of therapy sessions. These data can be collected using digital phenotyping and ecological momentary assessment techniques (i.e. repeated mood or scale measures via an app). This allows digital mental health tools and real-world data to inform therapists and enrich face-to-face sessions. This can be referred to as blended care/adjunctive therapy where service users can engage in ‘channel switching’ between digital and non-digital (face-to-face) interventions providing a more integrated service. This digital integration can be referred to as a kind of ‘digital glue’ that helps join up the in-person sessions with the real world. The paper presents the challenges, for example, the majority of mental health apps are maybe of inadequate quality and there is a lack of user retention. There are also ethical challenges, for example, with the perceived ‘over-promotion’ of screen-time and the perceived reduction in care when replacing humans with ‘computers’, and the trap of ‘technological solutionism’ whereby technology can be naively presumed to solve all problems. Finally, we argue for the need to take an evidence-based, systems thinking and co-production approach in the form of stakeholder-centred design when developing digital mental health services based on technologies. The main contribution of this paper is the integration of ideas from many different disciplines as well as the framework for blended care using ‘channel switching’ to showcase how digital data and technology can enrich physical services. Another contribution is the emergence of ‘poly-digital’ and a discussion on the challenges of digital mental health, specifically ‘digital ethics’.

BackgroundIn recent years, advances in technology have led to an influx of mental health apps, in particular the development of mental health and well-being chatbots, which have already shown promise in terms of their efficacy, availability, and accessibility. The ChatPal chatbot was developed to promote positive mental well-being among citizens living in rural areas. ChatPal is a multilingual chatbot, available in English, Scottish Gaelic, Swedish, and Finnish, containing psychoeducational content and exercises such as mindfulness and breathing, mood logging, gratitude, and thought diaries. ObjectiveThe primary objective of this study is to evaluate a multilingual mental health and well-being chatbot (ChatPal) to establish if it has an effect on mental well-being. Secondary objectives include investigating the characteristics of individuals that showed improvements in well-being along with those with worsening well-being and applying thematic analysis to user feedback. MethodsA pre-post intervention study was conducted where participants were recruited to use the intervention (ChatPal) for a 12-week period. Recruitment took place across 5 regions: Northern Ireland, Scotland, the Republic of Ireland, Sweden, and Finland. Outcome measures included the Short Warwick-Edinburgh Mental Well-Being Scale, the World Health Organization-Five Well-Being Index, and the Satisfaction with Life Scale, which were evaluated at baseline, midpoint, and end point. Written feedback was collected from participants and subjected to qualitative analysis to identify themes. ResultsA total of 348 people were recruited to the study (n=254, 73% female; n=94, 27% male) aged between 18 and 73 (mean 30) years. The well-being scores of participants improved from baseline to midpoint and from baseline to end point; however, improvement in scores was not statistically significant on the Short Warwick-Edinburgh Mental Well-Being Scale (P=.42), the World Health Organization-Five Well-Being Index (P=.52), or the Satisfaction With Life Scale (P=.81). Individuals that had improved well-being scores (n=16) interacted more with the chatbot and were significantly younger compared to those whose well-being declined over the study (P=.03). Three themes were identified from user feedback, including “positive experiences,” “mixed or neutral experiences,” and “negative experiences.” Positive experiences included enjoying exercises provided by the chatbot, while most of the mixed, neutral, or negative experiences mentioned liking the chatbot overall, but there were some barriers, such as technical or performance errors, that needed to be overcome. ConclusionsMarginal improvements in mental well-being were seen in those who used ChatPal, albeit nonsignificant. We propose that the chatbot could be used along with other service offerings to complement different digital or face-to-face services, although further research should be carried out to confirm the effectiveness of this approach. Nonetheless, this paper highlights the need for blended service offerings in mental health care.

Student mental health was a growing concern globally prior to the onset of the COVID-19 pandemic. The aim of this study was to assess the impact of the pandemic and associated restrictions on the psychological wellbeing of college students. Baseline data were collected pre-pandemic in September 2019 among students attending a university in Northern Ireland and an Institute of Technology in the Republic of Ireland. Surveys were also conducted with this cohort during the pandemic, at the start of the academic years 2020 and 2021 (499 students fully completed all three waves). A follow-up survey was conducted at the end of their third year, in summer 2022 (n = 229). High levels of mental health problems were already present among students commencing college. The subsequent pandemic had a very negative impact on student’s academic experience and other aspects of life. Rates of depression (PHQ-9) increased significantly from the onset of the pandemic and remained high. Anxiety (GAD-7) initially decreased but then escalated at the end of college. The study highlights the importance of early intervention and makes recommendations for addressing the needs of students during times of stress. Additional supports may be required to deal with the long-lasting impact of the pandemic.

This study aims to explore how machine learning classification accuracy changes with different demographic groups. The HappyDB is a dataset that contains over 100,000 happy statements, incorporating demographic information that includes marital status, gender, age, and parenthood status. Using the happiness category field, we test different types of machine learning classifiers to predict what category of happiness the statements belong to, for example, whether they indicate happiness relating to achievement or affection. The tests were initially conducted with three distinct classifiers and the best performing model was the convolutional neural network (CNN) model, which is a deep learning algorithm, achieving an F1 score of 0.897 when used with the complete dataset. This model was then used as the main classifier to further analyze the results and to establish any variety in performance when tested on different demographic groups. We analyzed the results to see if classification accuracy was improved for different demographic groups, and found that the accuracy of prediction within this dataset declined with age, with the exception of the single parent subgroup. The results also showed improved performance for the married and parent subgroups, and lower performances for the non-parent and un-married subgroups, even when investigating a balanced sample.