Adulteration and substitution of drugs purchased in Australia from cryptomarkets: An analysis of Test4Pay.

INTRODUCTION: Prohibited drugs in unregulated markets may be adulterated, resulting in increased risks for people who use drugs. This study investigated levels of drug adulteration and substitution of drugs purchased in Australia from cryptomarkets. METHODS: Data were collected from a darknet forum called Test4Pay from 1 September 2022 to 23 August 2023. Posts were included if they reported the results of drug samples submitted by post to the Vancouver-based Get Your Drugs Tested service, which uses Fourier-transform infrared spectroscopy with immunoassay strip tests (fentanyl and benzodiazepines). RESULTS: Of 103 samples, 65% contained only the advertised substance, 14% contained the advertised substance in combination with other psychoactive and/or potentially harmful substances and for 21%, the advertised substance was absent. Substances sold as MDMA, methamphetamine or heroin were consistently found to contain only the advertised substance, while substances sold as 2C-B, alprazolam or ketamine were the most likely to be completely substituted. Only 4 samples sold as cocaine contained solely the advertised substance, with 13 containing cocaine with adulterants like lidocaine, creatine, levamisole and boric acid (n = 19). No fentanyl contamination was detected. Novel dissociatives and novel benzodiazepines were detected, as well as a nitazene compound. DISCUSSION AND CONCLUSIONS: Drug markets under prohibition continue to contain numerous unexpected substances, some of which can elevate risk of harm. Cryptomarkets are not immune to this problem, despite review systems, which should, in theory, make vendors more accountable for the quality of their stock. These findings demonstrate a need for expansion of local drug checking services in Australia.

Corrigendum: A method and app for measuring the heterogeneous costs and benefits of justice processes.

[This corrects the article DOI: 10.3389/fpsyg.2023.1094303.].

A method and app for measuring the heterogeneous costs and benefits of justice processes.

Can the impact of justice processes be enhanced with the inclusion of a heterogeneous component into an existing cost-benefit analysis (CBA) APP that demonstrates how benefactors and beneficiaries are affected? Such a component requires: (i) moving beyond the traditional cost benefit conceptual framework of utilising averages; (ii) identification of social group or population-specific variation; (iii) identification of how justice processes differ across groups/populations; (iv) distribution of costs and benefits according to the identified variations; and (v) utilisation of empirically informed statistical techniques to gain new insights from data and maximise impact to beneficiaries. In this paper, we outline a method for capturing heterogeneity. We test our method and the CBA online APP we developed using primary data collected from a developmental crime prevention intervention in Australia. We identify how subgroups in the intervention display different behavioural adjustments across the reference period revealing the heterogeneous distribution of costs and benefits. Finally, we discuss the next version of the CBA APP, which incorporates an AI-driven component that reintegrates individual CBA projects using machine learning and other modern data science techniques. We argue that the APP, enhances CBA, development outcomes, and policy making efficiency for optimal prioritization of criminal justice resources. Further, the APP advances policy accessibility of enhanced, social group-specific data illuminating policy orientation for more inclusive, just, and resilient societal outcomes.

Financial Fraud against Older People in Hong Kong: Assessing and Predicting the Fear and Perceived Risk of Victimization.

While the majority of studies on the fear of crime focus on the impact of violent and property crimes at the population level, financial fraud against senior citizens is often under-investigated. This study uses data collected from 1061 older citizens in the community through a cross-sectional survey in Hong Kong to examine the levels of fear and perceived risk among Chinese senior citizens toward financial fraud and the factors behind them. Logistic regression analyses were conducted to assess the explanatory power of four theoretical perspectives (vulnerability, victimization, social integration, and satisfaction with police) on fear and perceived risk of fraud victimization. The results indicate significant predictive effects of victimization experience and satisfaction with police fairness and integrity on both the fear and the perceived risk of fraud among respondents. The findings not only confirm the differential impact of theoretical explanations on these constructs but can also contribute to crime prevention policy and practice in an aging society.

The impact of IoT security labelling on consumer product choice and willingness to pay.

The Internet of Things (IoT) brings internet connectivity to everyday electronic devices (e.g. security cameras and smart TVs) to improve their functionality and efficiency. However, serious security and privacy concerns have been raised about the IoT which impact upon consumer trust and purchasing. Moreover, devices vary considerably in terms of the security they provide, and it is difficult for consumers to differentiate between more and less secure devices. One proposal to address this is for devices to carry a security label to help consumers navigate the market and know which devices to trust, and to encourage manufacturers to improve security. Using a discrete choice experiment, we estimate the potential impact of such labels on participant's purchase decision making, along with device functionality and price. With the exception of a label that implied weak security, participants were significantly more likely to select a device that carried a label than one that did not. While they were generally willing to pay the most for premium functionality, for two of the labels tested, they were prepared to pay the same for security and functionality. Qualitative responses suggested that participants would use a label to inform purchasing decisions, and that the labels did not generate a false sense of security. Our findings suggest that the use of a security label represents a policy option that could influence behaviour and that should be seriously considered.

A methodological framework for coastal development assessment: A case study of Fujian Province, China.

Decision-makers often have to make trade-offs between economic growth and environmental conservation when developing and managing coastal environments. Coastal development and management need to be subject to rigorous assessments to determine if they are sustainable over time. We propose a methodological framework - the Coastal Development Index (CDI) for the assessment of the changes in sustainability of coastal development over time. CDI is a modified version of the Ocean Health Index (OHI) but with two new indicators - ecological and environmental indicators (EEI), and social and economic indicators (SEI), both of which comprise three sub-indicators (coastal protection, clean waters and species protection for EEI, and food provision, coastal livelihoods and economies and tourism and recreation for SEI). The six sub-indicators represent key aspects of coastal development and the level of exploitation of natural resources that have previously been missing in other conceptual frameworks. We demonstrate the value of CDI with a detailed case study of Fujian Province in China, 2000-2013. The scores of CDI decreased from 1.01 in 2000 to 0.42 in 2013 suggesting that the Fujian coastal zone has experienced unsustainable development in that time. Meanwhile, the scores of EEI decreased from 22.1 to 20.4 while the scores of SEI increased from 21.9 to 48.1 suggesting that environmental values have been eroded by economic growth. Analysis of the scores of sub-indicators reveals a need to integrate economic growth and social development with environmental conservation on Fujian coastal management. Our case study highlights the potential value of the CDI for improving the ecological sustainability of coastal zone management and development practices.

Towards a 'smart' cost-benefit tool: using machine learning to predict the costs of criminal justice policy interventions.

BACKGROUND: The Manning Cost-Benefit Tool (MCBT) was developed to assist criminal justice policymakers, policing organisations and crime prevention practitioners to assess the benefits of different interventions for reducing crime and to select those strategies that represent the greatest economic return on investment. DISCUSSION: A challenge with the MCBT and other cost-benefit tools is that users need to input, manually, a considerable amount of point-in-time data, a process that is time consuming, relies on subjective expert opinion, and introduces the potential for data-input error. In this paper, we present and discuss a conceptual model for a 'smart' MCBT that utilises machine learning techniques. SUMMARY: We argue that the Smart MCBT outlined in this paper will overcome the shortcomings of existing cost-benefit tools. It does this by reintegrating individual cost-benefit analysis (CBA) projects using a database system that securely stores and de-identifies project data, and redeploys it using a range of machine learning and data science techniques. In addition, the question of what works is respecified by the Smart MCBT tool as a data science pipeline, which serves to enhance CBA and reconfigure the policy making process in the paradigm of open data and data analytics.

Analysing pseudoephedrine/methamphetamine policy options in Australia using multi-criteria decision modelling.

BACKGROUND: In this paper we capture and synthesize the unique knowledge of experts so that choices regarding policy measures to address methamphetamine consumption and dependency in Australia can be strengthened. We examine perceptions of the: (1) influence of underlying factors that impact on the methamphetamine problem; (2) importance of various models of intervention that have the potential to affect the success of policies; and (3) efficacy of alternative pseudoephedrine policy options. METHODS: We adopt a multi-criteria decision model to unpack factors that affect decisions made by experts and examine potential variations on weight/preference among groups. Seventy experts from five groups (i.e. academia (18.6%), government and policy (27.1%), health (18.6%), pharmaceutical (17.1%) and police (18.6%)) in Australia participated in the survey. RESULTS: Social characteristics are considered the most important underlying factor, prevention the most effective strategy and Project STOP the most preferred policy option with respect to reducing methamphetamine consumption and dependency in Australia. One-way repeated ANOVAs indicate a statistically significant difference with regards to the influence of underlying factors (F(2.3, 144.5)=11.256, p<.001), effectiveness of interventions (F(2.4, 153.1)=28.738, p<.001) and policy options (F(2.8, 175.5)=70.854, p<.001). CONCLUSION: A majority of respondents believed that genetic, biological, emotional, cognitive and social factors are the most influential explanatory variables in terms of methamphetamine consumption and dependency. Most experts support the use of preventative mechanisms to inhibit drug initiation and delayed drug uptake. Compared to other policies, Project STOP (which aims to disrupt the initial diversion of pseudoephedrine) appears to be a more preferable preventative mechanism to control the production and subsequent sale and use of methamphetamine. This regulatory civil law lever engages third parties in controlling drug-related crime. The literature supports third-party partnerships as it engages experts who have knowledge and expertise with respect to prevention and harm minimization.

Understanding and Preventing Financial Fraud Against Older Citizens in Chinese Society: Results of a Focus Group Study.

This study examines the public discourse on financial fraud against older citizens in Chinese society using six focus groups with 45 stakeholders comprising social workers, police officers, caregivers, nurses, community representatives, and insurance and banking personnel. This study uses qualitative data to describe the patterns and social features that facilitate this specific type of crime in Hong Kong. The narratives of the community stakeholders reveal several specific social features of Hong Kong that contribute to financial fraud against older persons, such as traditional Chinese values and ideologies, increasing cross-border activities, the Internet and technological advancements, and unfavourable economic situations. The results of this study support the assumptions of the routine activity approach and have a number of theoretical and practical implications.