scholarly journals Big data: what it can and cannot achieve

2018 ◽  
Vol 24 (4) ◽  
pp. 237-244 ◽  
Author(s):  
Peter Schofield ◽  
Jayati Das-Munshi
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Health Research ◽  
Mental Health Research ◽  
Data Sources ◽  
List Type ◽  
Traditional Research ◽  
Research Designs ◽  
The Uk ◽  
Study Designs

SUMMARYThis article looks at the use of large datasets of health records, typically linked with other data sources, in mental health research. The most comprehensive examples of this kind of ‘big data’ are typically found in Scandinavian countries, although there are also many useful sources in the UK. There are a number of promising methodological innovations from studies using big data in UK mental health research, including: hybrid study designs, data linkage and enhanced study recruitment. It is, however, important to be aware of the limitations of research using big data, particularly the various pitfalls in analysis. We therefore caution against abandoning traditional research designs, and argue that other data sources are equally valuable and, ideally, research should incorporate data from a range of sources.LEARNING OBJECTIVES•Be aware of major big data resources relevant to mental health research•Be aware of key advantages and innovative study designs using these data sources•Understand the inherent limitations to studies reliant on big data aloneDECLARATION OF INTERESTNone.

Machinic Empathy and Mental Health: The Relational Ethics of Machine Empathy and Artificial Intelligence in Her

2019 ◽  
pp. 158-175
Author(s):  
Frances Shaw
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Health Research ◽  
Mental Health Treatment ◽  
Mental Health Research ◽  
Relational Ethics ◽  
Health Treatment

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

How much is spent on mental health research: developing a system for categorising grant funding in the UK

2019 ◽  
Vol 6 (5) ◽  
pp. 445-452 ◽  
Author(s):  
Eva Woelbert ◽  
Anne Kirtley ◽  
Neil Balmer ◽  
Sophie Dix
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Mental Health Research ◽  
Grant Funding ◽  
The Uk

scholarly journals Big data in mental health research – do the ns justify the means? Using large data-sets of electronic health records for mental health research

2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Data Storage ◽  
Health Research ◽  
Large Data ◽  
Population Data ◽  
Mental Health Research ◽  
Multiple Time ◽  
Short Account ◽  
Electronic Health

SummaryAdvances in information technology and data storage, so-called ‘big data’, have the potential to dramatically change the way we do research. We are presented with the possibility of whole-population data, collected over multiple time points and including detailed demographic information usually only available in expensive and labour-intensive surveys, but at a fraction of the cost and effort. Typically, accounts highlight the sheer volume of data available in terms of terabytes (1012) and petabytes (1015) of data while charting the exponential growth in computing power we can use to make sense of this. Presented with resources of such dizzying magnitude it is easy to lose sight of the potential limitations when the amount of data itself appears unlimited. In this short account I look at some recent advances in electronic health data that are relevant for mental health research while highlighting some of the potential pitfalls.

scholarly journals Participatory action research and oral history as natural allies in mental health research

2021 ◽  
pp. 146879412110399
Author(s):  
Verusca Calabria ◽  
Di Bailey
Keyword(s):  
Mental Health ◽  
Action Research ◽  
Participatory Action Research ◽  
Oral History ◽  
Health Research ◽  
Mental Health Research ◽  
Participatory Action ◽  
Mental Health Settings ◽  
Methodological Approaches ◽  
The Uk

This article explores the similarities and differences between oral history and participatory action research (PAR) as two qualitative research methods that both accord with an interpretivist paradigm. It examines how combining these two methodologies can benefit mental health research, offering opportunities for reflection and reciprocity. Drawing from the authors’ respective knowledge and experience of using oral history and PAR methods within social care and mental health settings in the UK, the article considers these opportunities in relation to key concepts, namely, the sharing of power, reciprocity and positionality that are inherent in both methodological approaches. The article concludes that PAR-led oral history offers a trans-disciplinary methodology that can offer fresh insights for improving practices and social outcomes and for reducing inequalities.

scholarly journals Looking Back to Move Forward: Reflections on the Strengths and Challenges of the COVID-19 UK Mental Health Research Response

2021 ◽  
Vol 12 ◽  
Author(s):  
Ola Demkowicz ◽  
Margarita Panayiotou ◽  
Sam Parsons ◽  
Amy Feltham ◽  
Louise Arseneault ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Research ◽  
Service Providers ◽  
Mental Health Research ◽  
Research Community ◽  
Long Term Effects ◽  
Ongoing Research ◽  
The Face ◽  
Short And Long Term ◽  
The Uk

In the face of the COVID-19 pandemic, the swift response of mental health research funders and institutions, service providers, and academics enabled progress toward understanding the mental health consequences. Nevertheless, there remains an urgent need to understand the true extent of the short- and long-term effects of the COVID-19 pandemic on mental health, necessitating ongoing research. Although the speed with which mental health researchers have mobilized to respond to the pandemic so far is to be commended, there are valid concerns as to whether speed may have compromised the quality of our work. As the pandemic continues to evolve, we must take time to reflect on our initial research response and collectively consider how we can use this to strengthen ensuing COVID-19 mental health research and our response to future crises. Here, we offer our reflections as members of the UK mental health research community to discuss the continuing progress and persisting challenges of our COVID-19 response, which we hope can encourage reflection and discussion among the wider research community. We conclude that (1) Fragmentation in our infrastructure has challenged the efficient, effective and equitable deployment of resources, (2) In responding quickly, we may have overlooked the role of experts by experience, (3) Robust and open methods may have been compromised by speedy responses, and (4) This pandemic may exacerbate existing issues of inequality in our workforce.

Using big data to advance mental health research

2020 ◽  
Vol 23 (1) ◽  
pp. 1-1
Author(s):  
Anne Duffy ◽  
Maria Faurholt-Jepsen ◽  
Michael Ostacher
Keyword(s):  
Mental Health ◽  
Big Data ◽  
Health Research ◽  
Mental Health Research
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