Unlabelled: To determine how early "the stuttering stereotype" is assigned, 160 university students rated a hypothetical vignette depicting either a 3-, 4-, 5-, or 6-year-old with or without the statement "He stutters". A factor analysis of the semantic differential scale showed a three-factor solution comprised of 17 of the 25 bi-polar adjective pairs. The factor labeled personality showed significantly more negative ratings for 2-, 4-, 5-, or 6-year-old children based on the inclusion of the "He stutters" sentence. There were no differences between male and female raters. A significant difference was found between raters who were knew someone who stuttered and raters who did not know someone who stuttered on the personality factor. Raters who were knew someone who stuttered were significantly more likely to assign more positive ratings to the children.
Learning Outcomes: Readers should be able to learn and understand: (1) the research describing the negative stereotypes associated with stuttering; (2) the vignette method used to evaluate stereotypes in children and youth; (3) the negative perceptions of the sentence "He stutters" on raters' perception of personality, sociability and speech for children as young as 3-, 4-, 5-, or 6-year-olds; and (4) the familiarity with a person who stutters and raters' perceptions of children who stutter.
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http://dx.doi.org/10.1016/j.jcomdis.2007.10.003 | DOI Listing |
J Neurol Neurosurg Psychiatry
December 2024
Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Background: Optical coherence tomography (OCT) inner retinal metrics reflect neurodegeneration in multiple sclerosis (MS). We explored OCT measures as biomarkers of disease severity in secondary progressive MS (SPMS).
Methods: We investigated people with SPMS from the Multiple Sclerosis-Secondary Progressive Multi-Arm Randomisation Trial OCT substudy, analysing brain MRIs, clinical assessments and OCT at baseline and 96 weeks.
Eur J Neurol
January 2025
Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK.
Background: Comorbidities including vascular risk factors can be associated with whole and regional brain atrophy in multiple sclerosis (MS). This has been examined in mixed MS cohorts in prospective or observational studies; however, the association between vascular comorbidities (VCM) in secondary progressive MS (SPMS) and brain atrophy has been less well studied. The aim was to investigate the cross-sectional and longitudinal association between VCM, comorbidity burden and brain atrophy in SPMS.
View Article and Find Full Text PDFBMJ Neurol Open
September 2024
Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia.
Background: The brain reserve hypothesis posits that larger maximal lifetime brain growth (MLBG) may confer protection against physical disability in multiple sclerosis (MS). Larger MLBG as a proxy for brain reserve, has been associated with reduced progression of physical disability in patients with early MS; however, it is unknown whether this association remains once in the secondary progressive phase of MS (SPMS). Our aim was to assess whether larger MLBG is associated with decreased physical disability progression in SPMS.
View Article and Find Full Text PDFBrain Commun
July 2024
Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, University College London (UCL) Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, WC1N 3BG, UK.
Crit Care
April 2024
Department of Critical Care Medicine, School of Medicine, University of Pittsburgh, 638 Scaife Hall, 3550 Terrace Street, Pittsburgh, PA, 15261, USA.
Background: Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven.
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