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Accepted Contribution:

Exploring diagnosis: the life cycles of 'seedling' psychiatric categories in EBM and data- 'gardens'  
Virginia Russell (University of Exeter)

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Short abstract:

Today, millions of people are using on-line tools to diagnose themselves; often receiving a diagnosis by e-mail. A review of new psychiatric diagnostic categories , how new diagnoses are created through datafication and become established via EBM, is the topic of this presentation.

Long abstract:

On-line tools and Apps have transformed our society. Today, millions of people are using on-line tools to diagnose themselves with various psychiatric conditions; often receiving a diagnosis by e-mail: a 'mailbox diagnosis'. The developmental lifecycle of psychiatric diagnostic categories themselves, how new diagnoses are created and become established via EBM, is the topic of my presentation.

On-line platforms have led to a proliferation of new ‘seedling’ diagnostic categories. They are nurtured by varying networks of associated research, industry, and lay support, which support more traditional EBM- like approaches (epidemiology studies) to validate seedling diagnoses. This presentation will start by reviewing which seedling diagnoses currently have a platform. They include more established diagnostic categories “mild cognitive impairment” (mild disorientation, forgetting names, dates: touted as forerunner to dementia); less established, e.g., “screen attention disorder” (limited attention caused by over-use of social media/screens), and new child diagnoses e.g., “pathological demand avoidance” (rigid resistance to action after being told what to do) and many more.

My aim is to introduce a 'life cycle' model of diagnostic category showing how both data harvesting and EBM interact to establish new diagnosis through a model that engenders fluid interdisciplinary thinking in the study of the social causes, construction, and implications of diagnostic categories.

Combined Format Open Panel P086
Navigating paradigms: between evidence-based and data-driven medicine
  Session 2 Wednesday 17 July, 2024, -