ABSTRACT
OBJECTIVES: This study aimed to understand young people's perception of the potential utility of arts and culture, focusing on online access, for supporting their mental health. DESIGN: A qualitative interview study. SETTING: Online. PARTICIPANTS: Participants were selected by purposeful sampling from an online survey of arts and culture for mental health and well-being. METHOD: Individual semi-structured interviews were conducted from 30 July 2020 to 9 September 2020. Rich interview data were analysed using reflexive thematic analysis. RESULTS: Thirteen participants aged 18-24 who were socio-demographically diverse and varied in their use of online arts and culture (OAC) and in their level of psychological distress were interviewed. Six themes, 'Characteristics of other activities', 'Online engagement', 'Human connection', 'Mechanisms of impact', 'Mental health outcomes' and 'Engagement optimisation', were identified along with subthemes. Participants identified that online engagement had some advantages over in-person engagement and benefits were greater with familiarity and regular use. Participants described that human connection was the feature of OAC most likely to benefit mental health and emphasised the importance of representation. Mechanisms included improving perspective, reflection, learning, escapism, creativity, exploration and discovery. Outcomes were described as the disruption of negative thought patterns, lifting of mood and increased feelings of calm and proactivity. CONCLUSIONS: This study demonstrates that young people have a critical level of insight and understanding regarding their mental health and ways in which it might be improved. These findings can be used to optimise the mental health benefits of OAC in an engaging and acceptable way for young people. These methodologies could be applied to other types of community resources for mental health.
Subject(s)
Emotions , Mental Health , Humans , Adolescent , Qualitative Research , AffectABSTRACT
AIMS AND METHOD: To gain a deeper understanding of the use of online culture and its potential benefits to mental health and well-being, sociodemographic characteristics and self-reported data on usage, perceived mental health benefits and health status were collected in an online cross-sectional survey during COVID-19 restrictions in the UK in June-July 2020. RESULTS: In total, 1056 people completed the survey. A high proportion of participants reported finding online culture helpful for mental health; all but one of the benefits were associated with regular use and some with age. Reported benefits were wide-ranging and interconnected. Those aged under 25 years were less likely to be regular users of online culture or to have increased their use during lockdown. CLINICAL IMPLICATIONS: There may be benefits in targeting cultural resources for mental health to vulnerable groups such as young adults.
ABSTRACT
BACKGROUND: Utilisation of routinely collected electronic health records from secondary care offers unprecedented possibilities for medical science research but can also present difficulties. One key issue is that medical information is presented as free-form text and, therefore, requires time commitment from clinicians to manually extract salient information. Natural language processing (NLP) methods can be used to automatically extract clinically relevant information. OBJECTIVE: Our aim is to use natural language processing (NLP) to capture real-world data on individuals with depression from the Clinical Record Interactive Search (CRIS) clinical text to foster the use of electronic healthcare data in mental health research. METHODS: We used a combination of methods to extract salient information from electronic health records. First, clinical experts define the information of interest and subsequently build the training and testing corpora for statistical models. Second, we built and fine-tuned the statistical models using active learning procedures. FINDINGS: Results show a high degree of accuracy in the extraction of drug-related information. Contrastingly, a much lower degree of accuracy is demonstrated in relation to auxiliary variables. In combination with state-of-the-art active learning paradigms, the performance of the model increases considerably. CONCLUSIONS: This study illustrates the feasibility of using the natural language processing models and proposes a research pipeline to be used for accurately extracting information from electronic health records. CLINICAL IMPLICATIONS: Real-world, individual patient data are an invaluable source of information, which can be used to better personalise treatment.