Erin Cvejic

Bio: Erin Cvejic is an academic researcher from University of Sydney. The author has contributed to research in topics: Health literacy & Medicine. The author has an hindex of 17, co-authored 93 publications receiving 931 citations. Previous affiliations of Erin Cvejic include University of Western Sydney & University of New South Wales.

Health literacy and disparities in COVID-19-related knowledge, attitudes, beliefs and behaviours in Australia.

Abstract: OBJECTIVES: To explore the variation in understanding of, attitudes towards, and uptake of, health advice on coronavirus disease 2019 (COVID-19) during the 2020 pandemic stage 3 restrictions ('lockdown') by health literacy in the Australian population. STUDY DESIGN: National cross-sectional community survey. SETTING: Australian general public. PARTICIPANTS: Adults aged over 18 years (N = 4362). MAIN OUTCOME MEASURES: Knowledge, attitudes and behaviours related to COVID-19; health literacy and sociodemographic factors. RESULTS: People with inadequate health literacy had poorer understanding of COVID-19 symptoms (49% vs 68%; p < 0.001), were less able to identify behaviours to prevent infection (59%% vs 72% p < 0.001), and experienced more difficulty finding information and understanding government messaging about COVID-19 than people with adequate health literacy. People with inadequate health literacy were less likely to rate social distancing as important (6.1 vs 6.5; p < 0.001) and reported more difficulty with remembering and accessing medicines since lockdown (3.6 vs 2.7; p < 0.001). People with lower health literacy were also more likely to endorse misinformed beliefs about COVID-19 and vaccinations (in general) than those with adequate health literacy. The same pattern of results was observed among people who primarily speak a language other than English at home. CONCLUSION: Our findings show that there are important disparities in COVID-19-related knowledge, attitudes and behaviours according to people's health literacy and language. These have the potential to undermine efforts to reduce viral transmission and may lead to social inequalities in health outcomes in Australia. People with the greatest burden of chronic disease are most disadvantaged, and are also most likely to experience severe disease and die from COVID-19. Addressing the health literacy, language and cultural needs of the community in public health messaging about COVID-19 must now be a priority in Australia.

People's Experiences and Satisfaction With Telehealth During the COVID-19 Pandemic in Australia: Cross-Sectional Survey Study.

Abstract: Background: In response to the COVID-19 pandemic, telehealth has rapidly been adopted to deliver health care services around the world. To date, studies have not compared people’s experiences with telehealth services during the pandemic in Australia to their experiences with traditional in-person visits. Objective: This study aimed to compare participants’ perceptions of telehealth consults to their perceptions of traditional in-person visits and investigate whether people believe that telehealth services would be useful after the pandemic. Methods: A national, cross-sectional, community survey was conducted between June 5 and June 12, 2020 in Australia. In total, 1369 participants who were aged ≥18 years and lived in Australia were recruited via targeted advertisements on social media (ie, Facebook and Instagram). Participants responded to survey questions about their telehealth experience, which included a free-text response option. A generalized linear model was used to estimate the adjusted relative risks of having a poorer telehealth experience than a traditional in-person visit experience. Content analysis was performed to determine the reasons why telehealth experiences were worse than traditional in-person visit experiences. Results: Of the 596 telehealth users, the majority of respondents (n=369, 61.9%) stated that their telehealth experience was “just as good as” or “better than” their traditional in-person medical appointment experience. On average, respondents perceived that telehealth would be moderately useful to very useful for medical appointments after the COVID-19 pandemic ends (mean 3.67, SD 1.1). Being male (P=.007), having a history of both depression and anxiety (P=.016), and lower patient activation scores (ie, individuals’ willingness to take on the role of managing their health/health care) (P=.036) were significantly associated with a poor telehealth experience. In total, 6 overarching themes were identified from free-text responses for why participants’ telehealth experiences were poorer than their traditional in-person medical appointment experiences, as follows: communication is not as effective, limitations with technology, issues with obtaining prescriptions and pathology results, reduced confidence in their doctor, additional burden for complex care, and inability to be physically examined. Conclusions: Based on our sample’s responses, telehealth appointment experiences were comparable to traditional in-person medical appointment experiences. Telehealth may be worthwhile as a mode of health care delivery while the pandemic continues, and it may continue to be worthwhile after the pandemic.

COVID-19 misinformation trends in Australia: Prospective longitudinal national survey

Abstract: Background: Misinformation about COVID-19 is common and has been spreading rapidly across the globe through social media platforms and other information systems. Understanding what the public knows about COVID-19 and identifying beliefs based on misinformation can help shape effective public health communications to ensure efforts to reduce viral transmission are not undermined. Objective: This study aimed to investigate the prevalence and factors associated with COVID-19 misinformation in Australia and their changes over time. Methods: This prospective, longitudinal national survey was completed by adults (18 years and above) across April (n=4362), May (n=1882), and June (n=1369) 2020. Results: Stronger agreement with misinformation was associated with younger age, male gender, lower education level, and language other than English spoken at home (P<.01 for all). After controlling for these variables, misinformation beliefs were significantly associated (P<.001) with lower levels of digital health literacy, perceived threat of COVID-19, confidence in government, and trust in scientific institutions. Analyses of specific government-identified misinformation revealed 3 clusters: prevention (associated with male gender and younger age), causation (associated with lower education level and greater social disadvantage), and cure (associated with younger age). Lower institutional trust and greater rejection of official government accounts were associated with stronger agreement with COVID-19 misinformation. Conclusions: The findings of this study highlight important gaps in communication effectiveness, which must be addressed to ensure effective COVID-19 prevention.

Translating fatigue to human performance

Abstract: Despite flourishing interest in the topic of fatigue-as indicated by the many presentations on fatigue at the 2015 Annual Meeting of the American College of Sports Medicine-surprisingly little is known about its effect on human performance. There are two main reasons for this dilemma: 1) the inability of current terminology to accommodate the scope of the conditions ascribed to fatigue, and 2) a paucity of validated experimental models. In contrast to current practice, a case is made for a unified definition of fatigue to facilitate its management in health and disease. On the basis of the classic two-domain concept of Mosso, fatigue is defined as a disabling symptom in which physical and cognitive function is limited by interactions between performance fatigability and perceived fatigability. As a symptom, fatigue can only be measured by self-report, quantified as either a trait characteristic or a state variable. One consequence of such a definition is that the word fatigue should not be preceded by an adjective (e.g., central, mental, muscle, peripheral, and supraspinal) to suggest the locus of the changes responsible for an observed level of fatigue. Rather, mechanistic studies should be performed with validated experimental models to identify the changes responsible for the reported fatigue. As indicated by three examples (walking endurance in old adults, time trials by endurance athletes, and fatigue in persons with multiple sclerosis) discussed in the review, however, it has proven challenging to develop valid experimental models of fatigue. The proposed framework provides a foundation to address the many gaps in knowledge of how laboratory measures of fatigue and fatigability affect real-world performance.

The computer program

Abstract: A computer program is a series of coded instructions for the computer to obey and represent a method of processing data. Programs can't be written in English. They must first be written using a special language called a programming language. A PROGRAMMING LANGUAGE (e.g. BASIC, PASCAL, and C+) consists of a set of codes and rules which can be used to construct commands for the computer. These commands are read and translated into electronic pulses needed to make the computer work. Programs are written by programmers. A computer language is a set of instructions used for writing computer programs. There are THREE (3) levels of languages: 1. MACHINE LANGUAGE – this was the first language available for programming. It varies from one computer to another, but the basic principles are the same. MACHINE LANGUAGE PROGRAMS are written using a series of 0's and 1's i.e. using a BINARY SYSTEM. All programs written today must be translated into machine language before they can be executed (used) by the computer. EXAMPLE: 110110001 2. ASSEMBLY LANGUAGE / LOW LEVEL LANGUAGE – these were developed to replace the 0's and 1's of machine language with symbols that are easier to understand and remember. Like with machine language, Assembly language varies form one make of computer to another so that a program written in one assembly language will not run on another make of computer. EXAMPLE: LDA 300 ADD 400 STA 500 3. HIGH LEVEL LANGUAGE – these differ from low level languages in that they require less coding detail and make programs easier to write. High level languages are designed for the solution of problems in one ore more areas of the application and are commonly described as application-oriented or problem-oriented languages. High level languages are not machine dependant. Programs written in a high level language must be translated to a form which can be accepted by that computer, i.e.