A comparison of rural Australian First Nations and Non-First Nations survey responses to COVID-19 risks and impacts: implications for health communications

Awareness and knowledge of drug decriminalization among people who use drugs in British Columbia: a multi-method pre-implementation study

BACKGROUND

In January 2023, British Columbia implemented a three-year exemption to Controlled Drugs and Substances Act, as granted by the federal government of Canada, to decriminalize the personal possession of small amounts of certain illegal drugs. This decriminalization policy, the first in Canada, was announced in response to the overdose emergency in British Columbia as a public health intervention that could help curb overdose deaths by reducing the impact of criminalization and increasing access to health and social services through stigma reduction.

METHODS

The current multi-method study examines people who use drugs' awareness and knowledge of British Columbia's decriminalization model through cross-sectional quantitative surveys and qualitative interviews among people who use drugs from September-November 2022, immediately prior to the implementation of decriminalization.

RESULTS

Quantitative findings show that two-thirds (63%) of people who use drugs were aware of the policy, but substantial knowledge gaps existed about the legal protections afforded (threshold amount, substances included, drug trafficking, confiscation). The qualitative findings suggest that people who use drugs misunderstood the details of the provincial decriminalization model and often conflated it with regulation. Results suggest that information sharing about decriminalization were minimal pre-implementation, highlighting areas for knowledge dissemination about people who use drugs' rights under this policy.

CONCLUSIONS

Given that decriminalization in British Columbia is a new and landmark reform, and that the success of decriminalization and its benefits may be undermined by poor awareness and knowledge of it, efforts to share information, increase understanding, and empower the community, may be required to promote its implementation and benefits for the community.

Development and validation of the COVID-19 Impact Scale in Australia

OBJECTIVE

The COVID-19 pandemic significantly impacted and continues to impact the health and well-being of Australian adults. However, there has been no instrument validated to comprehensively measure how the COVID-19 pandemic impacted adults in Australia across several domains (e.g. fear of COVID-19, attitudes towards vaccination, psychosocial impact of lockdowns).The current study conducted a rigorous psychometric process to develop and validate an instrument to measure the impact of the COVID-19 pandemic in Australia, the COVID-19 Impact Scale (CIS).

METHOD

Data was obtained from the Australian population. Participants (N = 563) aged between 19 and 91 years (M = 54.50, SD = 16.16) provided online responses between June, 2021 and May, 2022. The majority of participants were female (60.9%), employed either full-time (37.7%) or part-time (22.0%), and had completed an undergraduate degree or higher (70.1%). An initial pool of 30 items was developed based on a review of the literature and input from a panel of experts including psychologists, epidemiologists, and public health experts, among others. The study used network psychometrics to examine the psychometric properties of: (1) item score distributions; (2) item redundancy; (3) dimensionality; (4) model fit; (5) measurement invariance; (6) reliability; and (7) criterion validity.

RESULTS

Following an evaluation of items for ceiling/floor effects and redundancy, the final CIS network model included eighteen nodes and displayed a three-dimensional structure. The three communities of "Fear" (consisting of three nodes; ω = 0.82), "Attitudes" (consisting of ten nodes; ω = 0.89), and "Ill-being" (consisting of five nodes; ω = 0.79) displayed adequate reliability. The evaluation of model fit indicated a good fit of the network model (RMSEA = 0.047; CFI =0.98).

CONCLUSION

The instrument is available to be used by Australian researchers and implemented to evaluate public policies, adapted for future pandemics, or used internationally.

Public Risk Perception Attribution Model and Governance Path in COVID-19: A Perspective Based on Risk Information

Background

Risk perception is a key factor influencing the public’s behavioral response to major public health events. The research on public risk perception promotes the emergency management system to adapt to the needs of modern development. This article is based on a risk information perspective, using the COVID-19 event as an example. From the micro and macro perspectives, the influencing factors of public risk perception in major public health events in China are extracted, and the attribution model and index system of public risk perception are established.

Methods

In this paper, the five-level Likert scale is used to collect and measure the risk perception variable questionnaire through the combination of online and offline methods (a total of 550 questionnaires, the overall Alpha coefficient of the questionnaire is 0.955, and the KMO test coefficient t=0.941), and through independent samples t-test, correlation analysis, multiple regression analysis and other methods to draw relevant conclusions.

Results

The results showed that gender and age were significantly associated with risk perception (p<0.005), and education level was significantly negatively associated with risk perception (p <0 0.005). Risk information attention and risk perception were significantly positively correlated (p<0.005), media credibility was significantly positively correlated with risk perception (p<0.005), while risk information identification and media exposure had no significant interaction with risk perception (p=0.125, p=0.352).

Conclusion

Factors such as gender, age, education level, place of residence, media exposure, media credibility, risk information attention, and recognition lead to different levels of risk perception. This conclusion helps to provide a basis for relevant departments to conduct public risk management of major public health events based on differences in risk perceptions.