scholarly journals Invited Commentary on Stewart and Davis “ ‘Big data’ in mental health research—current status and emerging possibilities”

2016 ◽  
Vol 52 (2) ◽  
pp. 127-129 ◽  
Author(s):  
Jonathan D. Hafferty ◽  
Daniel J. Smith ◽  
Andrew M. McIntosh
Author(s):  
Frances Shaw

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.


2017 ◽  
Vol 41 (3) ◽  
pp. 129-132 ◽  
Author(s):  
Peter Schofield

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.


2018 ◽  
Vol 24 (4) ◽  
pp. 237-244 ◽  
Author(s):  
Peter Schofield ◽  
Jayati Das-Munshi

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.


2020 ◽  
Vol 23 (1) ◽  
pp. 1-1
Author(s):  
Anne Duffy ◽  
Maria Faurholt-Jepsen ◽  
Michael Ostacher

2019 ◽  
Vol 5 (1) ◽  
pp. 1-29 ◽  
Author(s):  
Olivia Banner

This article critiques today’s digital mental health research and treatment paradigms through a crip theoretical approach. I argue that, implemented in a neoliberal risk culture and austerity logics that implement big data to capacitate and debilitate, psychiatric technoscientific endeavors (what I call “technopsyience”) reproduce racial capitalism in their aim of governing mentalities.Yet mobile devices are also the means by which crip bodyminds creatively interrogate and resist racial capitalism and the psy discourses that support it. I explore a recent work of Afro-Surrealism that presents scenes of extractive racial capitalism, fantasies of digital futures, mental distress, and care, and that, I argue, opens up avenues for thinking bodyminds and the digital otherwise.


2019 ◽  
Vol 14 (4) ◽  
pp. 165-172
Author(s):  
Julie Morton ◽  
Michelle O’Reilly

Central to ethical debates in contemporary mental health research are the rhetoric of parity of esteem, challenges underpinned by the social construct of vulnerability and the tendency to homogenise the population diagnosed with mental health conditions. Such ethical dimensions are further complicated by the contemporary endeavour to work with ‘big data’ which has led to ambitious claims for discovery and knowledge. Research in mental health is challenging due to the perceived constraints of ethical principles such as the protection of autonomy, consent, risk and harms. This article discusses how ethical considerations need to be reconceptualised when using big data sets. The argument is foregrounded with an appraisal of the prevailing political discourse of parity of esteem demonstrating that ongoing disparities in services and research should also be considered when inquiry uses big data.


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