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Nick Shryane

Researcher at University of Manchester

Publications -  42
Citations -  1683

Nick Shryane is an academic researcher from University of Manchester. The author has contributed to research in topics: Paranoia & Population. The author has an hindex of 16, co-authored 39 publications receiving 1326 citations. Previous affiliations of Nick Shryane include Greater Manchester West Mental Health NHS Foundation Trust & University of Hull.

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The QCAE: A Questionnaire of Cognitive and Affective Empathy

TL;DR: The hypothesized 2-factor structure (cognitive and affective empathy) was tested and provided the best and most parsimonious fit to the data.
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The cognitive and affective structure of paranoid delusions: a transdiagnostic investigation of patients with schizophrenia spectrum disorders and depression.

TL;DR: It is indicated that paranoid delusions are associated with a combination of pessimistic thinking style (low self-esteem, pessimistic explanatory style, and negative emotion) and impaired cognitive performance and treatment for paranoid patients should address both types of processes.
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Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning.

TL;DR: The utility of three complementary methods for predicting hospital length of stay (LoS) using UK national- and hospital-level data is demonstrated and data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.
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The persecution and deservedness scale.

TL;DR: The PaDS is a reliable and valid measure of paranoid thinking and perceived deservedness of persecution, which is sensitive for use in clinical and non-clinical populations.
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Modeling the effects of combining diverse software fault detection techniques

TL;DR: It is shown that many of these results for design diversity have counterparts in diverse fault detection in a single software version, and it is possible for effectiveness to be even greater than it would be under an assumption of statistical independence.