Journal of Intellectual Disability Research · 1998 · 180 citations · 26 references
MedicinePsychiatryPsychologyDementiaIntellectual DisabilitiesPsychiatric AssessmentDisability AssessmentDisability EvaluationMental IllnessNeurodevelopmental DisordersPsychological TestingIntellectual Disability IdentificationIntellectual Disability ReferralInternal Consistency ReliabilityInterrater AgreementPervasive Developmental DisorderRecognition Of Psychiatric SymptomsReferral DecisionsUnspecified Anxiety Disorder
The Mini PAS-ADD is an assessment schedule for psychiatric disorders in people with an intellectual disability. It is designed to provide a link between the mental health expertise of psychiatrists and psychologists, and the detailed knowledge of individual service users possessed by support staff. In broad terms, the aim of the Mini PAS-ADD is to enable non-psychiatrists accurately to recognize clinically significant psychiatric disorders in the people who they care for, so that they can make informed referral decisions. The instrument comprises 86 psychiatric symptoms and generates a series of subscores on: depression, anxiety and phobias, mania, obsessive-compulsive disorder, psychosis, unspecified disorder (including dementia), and pervasive developmental disorder (autism). The present paper reports the results of a study investigating internal consistency, inter-rater agreement and validity in relation to clinical opinion, using a sample of 68 people with intellectual disability who were in contact with psychiatric services. In terms of the instrument fulfilling its main intended function, i.e. accurate case recognition, the crucial question was whether the support workers, with their lesser knowledge of psychopathology, were also able to correctly identify cases identified by expert clinicians. The validity results in this respect (81% agreement on case recognition) were sufficiently good that it is to be anticipated that the Mini PAS-ADD should have a significant impact on the identification of psychiatric disorders in the community of people with intellectual disability.
26
Scikit-learn: Machine Learning in Python
Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort et al. · arXiv (Cornell University) · 2012 · 63.7K citations · Full text
SciPy 1.0: fundamental algorithms for scientific computing in Python
Nature Methods · 2020 · 37.9K citations · Full text
David H. Brainard · Spatial Vision · 1997 · 20.1K citations
M Kleiner, D Brainard, D Pelli · MPG.PuRe (Max Planck Society) · 2007 · 3.8K citations · Full text