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Sarah M. Goodday

Researcher at University of Oxford

Publications -  44
Citations -  653

Sarah M. Goodday is an academic researcher from University of Oxford. The author has contributed to research in topics: Bipolar disorder & Offspring. The author has an hindex of 11, co-authored 38 publications receiving 395 citations. Previous affiliations of Sarah M. Goodday include University of Toronto.

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Antecedents and sex/gender differences in youth suicidal behavior

TL;DR: These proposed antecedents to youth suicide highlight the importance of interventions that alter early environment(s) and/or one's ability to adapt to them, and may have more enduring protective effects, for the individual and for future generations, if implemented in youth.
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The Emergent Course of Bipolar Disorder: Observations Over Two Decades From the Canadian High-Risk Offspring Cohort

TL;DR: Bipolar disorder in individuals at familial risk typically unfolds in a progressive clinical sequence, and childhood sleep and anxiety disorders are important predictors, as are clinically significant mood symptoms and psychotic symptoms in depressive episodes.
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Candidate Risks Indicators for Bipolar Disorder: Early Intervention Opportunities in High-Risk Youth

TL;DR: A selective review of findings from studies of high-risk children of affected parents that inform the knowledge of illness risk and development markers of bipolar disorder to identify candidate clinical, biological, and psychological risk indicators that could serve as targets for future early intervention and prevention studies.
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Predictors of mental health and academic outcomes in first-year university students: Identifying prevention and early-intervention targets.

TL;DR: Clinically significant mental health symptoms are common and persistent among first-year university students and have a negative impact on academic performance and well-being, and a comprehensive mental health strategy that includes a whole university approach to prevention and targeted early-intervention measures is justified.
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Unlocking stress and forecasting its consequences with digital technology.

TL;DR: The growth and availability of digital technologies involving wearable devices and mobile phone apps afford the opportunity to dramatically improve measurement of the biological stress response in real time, and the marriage of artificial intelligence (AI) and machine learning could dramatically enhance the field of stress research.