Online arts and culture for mental health in young people: a qualitative interview study

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.

Use of online cultural content for mental health and well-being during COVID-19 restrictions: cross-sectional survey

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.

Natural language processing for structuring clinical text data on depression using UK-CRIS

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.

Characterization of the region containing the jcpk PKD gene on mouse Chromosome 10

The jcpk gene on mouse Chromosome 10 causes a severe, early onset form of polycystic kidney disease (PKD) when inherited in an autosomal recessive manner. In order to positionally clone this gene, high resolution genetic and radiation hybrid maps were generated along with a detailed physical map of the approximately 500-kb region containing the jcpk gene. Additionally, sixty-nine kidney-specific ESTs were evaluated as candidates for jcpk and subsequently localized throughout the mouse genome by radiation hybrid mapping analysis. Previous studies indicating non-complementation of the jcpk mutation and 67Gso, a new PKD translocation mutant had suggested that 67Gso represents a new allele of jcpk. Fluorescence in situ hybridization (FISH) analysis using key bacterial artificial chromosome clones from the jcpk critical region, refined the 67Gso breakpoint and provided support for the allelism of jcpk and 67Gso.