The Gaming Disorder Identification Test (GADIT) - A screening tool for Gaming Disorder based on ICD-11

被引:3
作者
Chan, Gary C. k. [1 ,2 ]
Saunders, John B. [1 ]
Stjepanovic, Daniel [1 ,2 ]
Mcclure-Thomas, Caitlin [1 ,2 ]
Connor, Jason [1 ,2 ,8 ]
Hides, Leanne [1 ,2 ]
Wood, Andrew [3 ]
King, Daniel [4 ]
Siste, Kristiana [5 ,6 ]
Long, Jiang [7 ]
Leung, Janni k. [1 ,2 ]
机构
[1] Univ Queensland, Natl Ctr Youth Subst Use Res, St Lucia, Australia
[2] Univ Queensland, Sch Psychol, St Lucia, Australia
[3] Griffith Univ, Sch Appl Psychol, Brisbane, Australia
[4] Flinders Univ S Australia, Coll Educ Psychol & Social Work, Bedford Pk, Australia
[5] Univ Indonesia, Fac Med, Dept Psychiat, Depok, Indonesia
[6] Dr Cipto Mangunkusumo Gen Hosp, Jakarta, Indonesia
[7] Shanghai Jiao Tong Univ, Sch Med, Shanghai Mental Hlth Ctr, Shanghai, Peoples R China
[8] Univ Queensland, Fac Med, Discipline Psychiat, Herston, Australia
基金
澳大利亚国家健康与医学研究理事会;
关键词
gaming disorder; psychometrics; reliability; validity; scale development; screening tool; LANGUAGE;
D O I
10.1556/2006.2024.00038
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Background: Gaming Disorder was included as an addictive disorder in the latest version of the International Classification of Diseases (ICD-11), published in 2022. The present study aimed to develop a screening tool for Gaming Disorder, the Gaming Disorder Identification Test (GADIT), based on the four ICD-11 diagnostic criteria: impaired control, increasing priority, continued gaming despite harm, and functional impairment. Method: We reviewed 297 questionnaire items from 48 existing gaming addiction scales and selected 68 items based on content validity. Two datasets were collected: 1) an online panel (N = 803) from Australia, United States, United Kingdom and Canada, split into a development set (N = 589) and a validation dataset (N = 214); and 2) a university sample (N = 408) from Australia. Item response theory and confirmatory factor analyses were conducted to select eight items to form the GADIT. Validity was established by regressing the GADIT against known correlates of Gaming Disorder. Results: Confirmatory factor analyses of the GADIT showed good model fi t (RMSEA=<0.001-0.108; CFI = 0.98-1.00), and internal consistency was excellent (Cronbach's alphas = 0.77-0.92). GADIT scores were strongly associated with the Internet Gaming Disorder Test (IGDT-10), and significantly associated with gaming intensity, eye fatigue, hand pain, wrist pain, back or neck pain, and excessive in-game purchases, in both the validation and the university sample datasets. Conclusion: The GADIT has strong psychometric properties in two independent samples from four English-speaking countries collected through different channels, and shown validity against existing scales and variables that are associated with Gaming Disorder. A cut-off of 5 is tentatively recommended for screening for Gaming Disorder.
引用
收藏
页码:729 / 741
页数:13
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