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Ketamine is a restricted and regulated medication in Australia and New Zealand, which has implications when considering treatment for patients with treatment-resistant depression and a history of illicit drug use, abuse or dependence. Regulations governing prescription of ketamine for treatment-resistant depression vary between jurisdictions in Australia and New Zealand, though most restrict use in those with drug dependence. There is substantial variation in definitions of drug dependence used in each jurisdiction, and between the legal and clinical definitions, with the latter specified in the current International Classification of Diseases, Eleventh Revision and Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. This paper reviews the literature assessing the risk of ketamine misuse and dependence in patients with a history of illicit drug use, abuse or dependence and presents recommendations for psychiatrists who prescribe ketamine in such patients with treatment-resistant depression. [ABSTRACT FROM AUTHOR]

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. 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. Confirmatory factor analyses of the GADIT showed good model fit (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. 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. [ABSTRACT FROM AUTHOR]