Predicting Work Outcome in Patients with Schizophrenia

2019 Fiscal Year Final Research Report

• Project/Area Number: 15K04109
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Research Field: Clinical psychology
• Research Institution: Fukushima University
• Principal Investigator: Sumiyoshi Chika 福島大学, 人間発達文化学類, 教授 (20262347)
• Project Period (FY): 2015-04-01 – 2020-03-31
• Keywords: 統合失調症 / 機能的転帰 / 高次認知機能 / 労働 / 意味記憶 / カテゴリ流暢性

Outline of Final Research Achievements

The purposes of the study were to investigate factors to predict work outcome and to examine the accuracy of the perdition in patients with schizophrenia. The method to evaluate higher-order cognition was also studied to explore the effective variable(s) in the prediction. Logistic regression analysis showed that cognitive decline, psychiatric symptoms, and social function were significant variables to predict work status (work hours per week) in patients with schizophrenia. Overall, estimation was accurate yielding 70-80% accuracy. Singular value decomposition (SVD) analysis was applied to category fluency data to explore effective variable(s) of higher-order cognition to be used in predicting work outcome. The SVD analysis suggested that cosine value was the useful variable, revealing irregularity of the semantic memory organization in patients with schizophrenia.

Free Research Field

認知心理学、臨床心理学

Academic Significance and Societal Importance of the Research Achievements

社会的意義：本邦での精神疾患患者の就労状況は、欧米に比べ良好とはいえない。本研究は、精神疾患患者の労働状態について客観的データを提供することにより、研究面から当事者・雇用者の支援に寄与する。
学術的意義：本研究では、統合失調症患者の高次認知機能評価指標を得るために、テキストマイニング手法（特異値分解法）を試みた。従来、主として人文科学で利用されてきた同手法の精神医学分野への応用は、文理融合型研究を推進するものである。