Lindy J. Williams
Adelaide, SA, Australia
Last Updated: 09/26/2026
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Found 20 publications
International multidisciplinary consensus recommendations for the screening and assessment of post-stroke spatial neglect using an online Delphi method.
Journal: Clinical rehabilitation
Published: August 03, 2026
An International and Multidisciplinary Consensus on the Labeling of Spatial Neglect Using a Modified Delphi Method.
Journal: Archives of rehabilitation research and clinical translation
Published: July 15, 2024
Cost-effectiveness of neuropsychological rehabilitation for acquired brain injuries: Update of Stolwyk et al.'s (2019) review.
Journal: Journal of neuropsychology
Published: June 08, 2024
Identifying spatial neglect - an updated systematic review of the psychometric properties of assessment tools in adults post-stroke.
Journal: Neuropsychological rehabilitation
Published: May 10, 2024
Key constructs of body awareness impairments post-stroke: a scoping review of assessment tools and interventions.
Journal: Disability and rehabilitation
Published: October 03, 2022
Rapid screening for neglect following stroke: A systematic search and European Academy of Neurology recommendations.
Journal: European journal of neurology
Published: February 08, 2022
Spatial Neglect Subtypes, Definitions and Assessment Tools: A Scoping Review.
Journal: Frontiers in neurology
Published: July 16, 2021
Assistance dogs in occupational therapy practice: A survey of Australian occupational therapists' experiences and recommendations.
Journal: Australian occupational therapy journal
Published: May 06, 2021
Successful Islet Outcomes Using Australia-Wide Donors: A National Centre Experience.
Journal: Metabolites
Published: April 21, 2021
Australian parents' experiences of owning an autism assistance dog.
Journal: Health & social care in the community
Published: January 17, 2021
Repetitive transcranial magnetic stimulation for post-stroke depression: a randomised trial with neurophysiological insight.
Journal: Journal of neurology
Published: October 18, 2020
Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.
Journal: European radiology
Published: July 28, 2020
Last Updated: 09/26/2026