Back to Search Start Over

A 30-year retrospective case analysis in the Delphi of cognitive rehabilitation therapy.

Authors :
Finley, John-Christopher
Parente, Frederick
Source :
Technological Forecasting & Social Change; Jan2019, Vol. 138, p254-260, 7p
Publication Year :
2019

Abstract

Abstract In 1987, Parente used the Delphi method to predict changes in the field of cognitive rehabilitation therapy (CRT). Fifty licensed professionals provided predictions about the likely occurrence and probable time courses for 31 scenarios that could possibly have occurred over the 30-year interval between 1987 and 2000+. It has now been 30 years since the initial polling; thus, the purpose of this study was to evaluate the accuracy of these Delphic predictions, via two validation methods. First, we contacted and reviewed statistical information from nationwide data bases (i.e., Center for Disease Control and Prevention, and the Brain Injury Association of America) to see If the scenarios occurred. Second, we polled 12 additional professionals, most of whom had practiced in the field of CRT during the polling period and who still maintained an active practice to assess When the various remaining scenarios had occurred. In this study, probability of occurrence accuracy was approximately 80%, although there was a significant bias towards false positives. Time course predictions were accurate within 1–5 years, although there was a general bias towards underestimating the occurrence of the events. Highlights • The Delphi method provides accurate predictions across a 30-year time span. • The Delphi method predicts whether an event will occur with 80% accuracy. • The Delphi method predicts when an event will occur within a timeframe of one to five years. • The Delphi method can help understand the developing trends among the field of Cognitive Rehabilitation Therapy. • Polling a group of experts and using public domain statistics permits an effective and accurate cross-validation of results. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00401625
Volume :
138
Database :
Supplemental Index
Journal :
Technological Forecasting & Social Change
Publication Type :
Academic Journal
Accession number :
133258136
Full Text :
https://doi.org/10.1016/j.techfore.2018.09.022