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Objective: With increasing levels of cultural diversity, it is important that mental health services are relevant and accessible to new migrant populations. Efforts have been made to bring attention to the unique experiences and needs of culturally and linguistically diverse (CALD) sub-groups in clinical settings, resulting in various frameworks and professional development workshops. While some of these strategies have raised awareness and persuaded service providers to accommodate different populations, few are effective and genuinely impact consumer outcomes. Conclusion: This paper proposes an integrated cross-cultural assessment framework comprising both clinical and organisational components to improve cross-cultural clinical encounters and consumer satisfaction. The framework underscores the importance of the therapeutic alliance through the building of rapport and trust. Moreover, the framework is designed to be organisationally feasible, and locally and practically oriented. [ABSTRACT FROM AUTHOR]

Australia's fast-growing migrant population encompasses many groups from culturally and linguistically diverse backgrounds. It is well documented that these groups experience varying pre- and post-migratory challenges. Despite this knowledge, little is known about the extent to which these groups experience and perceive violence and how or whether they seek assistance after such incidents. It is important to identify any potential discrepancies to ensure that services can provide the most targeted supports to victims. Data were collected from the 2016 Australian Bureau of Statistics Public Safety Survey. Using chi-square tests, prevalence, experiences, and perceptions of violence occurring after the age of 15, postincident support, reporting behaviors, and health variables related to violent incidents were compared across three cultural groups arranged by region of birth: born in Australia (BIA), born overseas in main English-speaking countries (BNMESC), and born overseas in non-English-speaking countries (BOC). BOC individuals reported much lower rates of violent victimization compared to BIA and BMSEC individuals. More than two-thirds of each cultural group did not report their most recent experience of violence to police. Violence was most commonly experienced at home, although a higher proportion of BIA individuals experienced violence at an entertainment venue, and a higher proportion of BOC individuals experienced violence outside (i.e., in the street). The contribution of Alcohol/Substances was much higher for BIA and BMESC compared to BOC individuals. BOC individuals experienced more anxiety post-incident, while more BIA individuals sustained physical injuries. A similar proportion of each group sought assistance post-incident, however, more BOC individuals had never told anyone about the incident. Perceptions of the violent incident were generally similar across groups, though fewer BOC individuals perceived the incident to be a crime. Some differences were apparent across cultural groups regarding the prevalence, experiences and reporting of violent incidents. Implications and future research directions are discussed within. [ABSTRACT FROM AUTHOR]

The use of statistical learning methods has recently increased within the risk assessment literature. They have primarily been used to increase accuracy and the area under the curve (AUC, i.e., discrimination). Processing approaches applied to statistical learning methods have also emerged to increase cross-cultural fairness. However, these approaches are rarely trialed in the forensic psychology discipline nor have they been trialed as an approach to increase fairness in Australia. The study included 380 Aboriginal and Torres Strait Islander and non-Aboriginal and Torres Strait Islander males assessed with the Level of Service/Risk Needs Responsivity (LS/RNR). Discrimination was assessed through the AUC, and fairness was assessed through the cross area under the curve (xAUC), error rate balance, calibration, predictive parity, and statistical parity. Logistic regression, penalized logistic regression, random forest, stochastic gradient boosting, and support vector machine algorithms using the LS/RNR risk factors were used to compare performance against the LS/RNR total risk score. The algorithms were then subjected to pre- and postprocessing approaches to see if fairness could be improved. Statistical learning methods were found to produce comparable or marginally improved AUC values. Processing approaches increased several fairness definitions (namely xAUC, error rate balance, and statistical parity) between Aboriginal and Torres Strait Islanders and non-Aboriginal and Torres Strait Islanders. The findings demonstrate that statistical learning methods may be a useful approach to increasing the discrimination and cross-cultural fairness of risk assessment instruments. However, both fairness and the use of statistical learning methods encompass significant trade-offs that need to be considered. Public Significance Statement: The purpose of this study is to investigate the use of algorithms in increasing fairness between cultural groups by utilizing a commonly used instrument that assesses the risk of reoffending. Although this preliminary study demonstrated positive findings in increasing cross-cultural fairness, various trade-offs emerged, including among different forms of fairness, that require thoughtful deliberation. [ABSTRACT FROM AUTHOR]