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Objectives: Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists. Methods: A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age ≥ 18 years and series slice thickness ≤ 1.5 mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard. Results: The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced. Conclusions: The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation. Clinical relevance statement: This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care. Key Points: • This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans. • The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance. • The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain. [ABSTRACT FROM AUTHOR]

Although visual arts pedagogies are considered central within early childhood education programs, teacher self-efficacy has a direct impact on the quality and delivery of visual arts curricula. Until recently, the visual arts self-efficacy, pedagogical knowledge, and practice of in-service early childhood teachers have remained largely unexplored. The authors of this paper present a qualitative, iterative re-analysis of their three PhD studies which broadly focussed on early childhood visual arts praxis and specifically examined the visual arts beliefs and pedagogy of early childhood teachers in Australia and New Zealand. A thematic analysis of the intersecting self-efficacy findings raised in the three studies identifies the powerful influence of self-efficacy on teaching practice in the visual arts domain and offers new understandings about visual arts self-efficacy amongst early childhood teachers. The combined findings reveal several factors that restrain or enhance teachers' visual arts self-efficacy across time, including the impact of childhood experiences, pre-service training and epistemological beliefs. The paper also identifies several enabling conditions that appear to support teachers to develop and maintain positive visual arts self-efficacy beliefs, including practical engagement with materials, sustained professional learning, relational trust and intentional leadership. These enabling conditions offer practical strategies and research recommendations in service of positive visual arts self-efficacy to enhance quality visual arts teaching in early childhood contexts. [ABSTRACT FROM AUTHOR]

Commensal relationships between wild plants and their dispersers play a key ecological and evolutionary role in community structure and function. While non-human dispersers are often considered critical to plant recruitment, human dispersers have received much less attention, especially when it comes to non-domesticated plants. Australia, as a continent historically characterized by economies reliant on non-domesticated plants, is thus a key system for exploring the ecological role of people as seed dispersers in the absence of agriculture. Here, we utilize a controlled observation research design, employing ecological surveys and ethnographic observations to examine how seed dispersal and landscape burning by Martu Aboriginal people affects the distribution of three preferred plants and one (edible, but non-preferred) control species. Using an information theoretic approach, we find that the three preferred plants show evidence of human dispersal, with the strongest evidence supporting anthropogenic dispersal for the wild bush tomato, Solanum diversiflorum. Human dispersal of wild plants has received limited attention. Here, the authors combine ecological surveys and ethnographic observations to examine how Martu Aboriginal people's seed dispersal and landscape burning impact plant distribution. [ABSTRACT FROM AUTHOR]

Despite efforts to foster a more equitable gender representation, Australia's higher education sector and workforce continue to be highly segregated. This article focusses on the gendered experiences of first-in-family (FIF) students—many who are from low-socioeconomic communities—transitioning to Australian universities. In terms of the gendered nature of widening participation, we know students who are FIF will often study disciplines which align with traditional gender norms. Drawing on the First-in-Family Project (n = 48), we present the analysis of our findings in two parts. First, we provide an overview of the cohort where we analyse the gendering of degree choice of FIF students. Second, underpinned by theoretical work focussed on student gendered and classed subjectivities, we address how FIF young people negotiated classed and gendered norms during the transition to university. Our data suggest that young people engage in 'gender work' and 'class work' which informs their identities and how they make choices about their futures. Based on our research, we argue that to achieve social justice there is a need for a renewed attention to gender within the widening participation agenda. [ABSTRACT FROM AUTHOR]

Scholars in critical masculinities studies argue that we need men involved and engaged in gender equity movements for gender justice to be realised. Yet we need to know more about how different groups of men are understanding gender equity and what the barriers might be. Amidst significant media interest in elite private boys' schooling and its possible (re)production of sexist cultures, this paper explores how 13 men who attended such schools in Australia between the 1970s and the 2000s understand gender justice, revealing a diversity of positions and practices across the different generational groups. We argue the men's engagements with gender justice are shaped by a broad 'curriculum of privilege' including school and non-school based experiences that mediates their lives. Further research with both elite boys' schools and their alumni is needed to better understand generational change in their engagements with gender justice. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) tools, notably ChatGPT, are increasingly recognised for their transformative potential in higher education. This study employs a detailed case study approach complemented by a survey, delving into ChatGPT's impact on pedagogical practices, student engagement, and academic performance. It involved 74 undergraduate and postgraduate students enrolled in data analytics courses in Australia. The quantitative analysis highlights ChatGPT's role in providing personalised and on-demand support, which is highly valued among users for its flexibility and responsiveness, meeting a critical demand in educational settings. Notably, the study identifies a medium effect size ( η 2 = 0.173 ) in perceived benefits, indicating that ChatGPT accounts for approximately 17.3% of the variance in improved academic outcomes. However, challenges such as ChatGPT's limited understanding of complex queries and the lack of human interactions are primary concerns, with a medium effect size ( η 2 = 0.289 ) suggesting significant areas for improvement. Furthermore, statistical analyses reveal a clear relationship between the frequency of ChatGPT usage and the perception of its benefits, underscoring the transformative potential for users who have integrated it into their academic practices. Despite these challenges, the differential impact on users versus non-users highlights the potential for ChatGPT to foster more engaging and effective educational practices. The findings advocate for targeted strategies to epitomise ChatGPT's integration into educational settings, emphasising the need for ongoing research and the development of comprehensive guidelines to navigate its complexities and maximise its educational benefits. [ABSTRACT FROM AUTHOR]

Research on gender issues and mathematics education is often conducted in classroom settings and/or with teachers, students, and parents. However, perspectives about mathematics from adults beyond teachers and parents can have an impact on students. Thus, we conducted research in Australia and Canada about the general public's views of gender and mathematics. Participants (n = 405) were surveyed using a questionnaire in which all questions were worded in a non-binary manner. In this article, we focus on participants' views about gender and mathematics ability, and the importance of studying mathematics by gender. We report both overall trends and trends by demographic group (country, gender, age, and education level). The majority of participants indicated that there was no relationship between gender and mathematics ability or between gender and the importance of studying mathematics. Participants with gendered views typically felt that boys/men/males are better at mathematics and that it was more important for girls/women/females to study mathematics. Although the findings were generally encouraging, the existence of sexist, stereotyped views highlights the need for additional work to probe people's views of gender and mathematics. Our study is an example of mathematics education research conducted in a gender-inclusive way. [ABSTRACT FROM AUTHOR]

In recognition memory, the variance of the target distribution is almost universally found to be greater than that of the lure distribution. However, these estimates commonly come from long-term memory paradigms where words are used as stimuli. Two exceptions to this rule have found evidence for greater lure variability: a short-term memory task (Yotsumoto et al., Memory & Cognition,36, 282–294 2008) and in an eyewitness memory paradigm (Wixted et al., Cognitive Psychology,105, 81–114 2018). In the present work, we conducted a series of recognition memory experiments using different stimulus (faces vs. words) along with different paradigms (long-term vs. short-term paradigms) to evaluate whether either of these conditions would result in greater variability in lure items. Greater target variability was observed across stimulus types and memory paradigms. This suggests that factors other than stimuli and retention interval might be responsible for cases where variability is less for targets than lures. [ABSTRACT FROM AUTHOR]

The introduction of statistical concepts into school curricula in Australia and New Zealand in the early 1990s initiated an ongoing research program into the learning and teaching of statistics and probability in both countries. This paper reviews the contribution of Australian and New Zealand researchers to building statistical literacy at school, alongside international developments. From recognising how students develop understanding of specific statistical and probabilistic concepts, through teacher knowledge and beliefs for teaching statistics, to intervention studies and targeted teaching, the field of statistics education has grown and changed. Statistics and probability are now well established as part of the mathematics curriculum. The importance of linking statistical literacy and statistical understanding across the curriculum, as well as in STEM, has also begun to receive attention as other subjects have recognised the importance of data in their fields. Following a comprehensive review of the field in Australia and New Zealand, this paper then considers emerging areas of interest, such as new approaches to data visualisation, and suggests future research. [ABSTRACT FROM AUTHOR]

The teaching and learning of statistical thinking begins at a young age in Australia, with a focus on data representation and interpretation from Foundation Year (age 5), and the collection, sorting and categorising of items from the natural environment starting even earlier. The intangible concept of data, as part of statistical literacy, can be complex for children to grasp, especially when applying the notion of data to the everyday world or when data are explored in isolation to an investigation process. Authentic data modelling experiences present meaningful opportunities to apply statistical thinking although expert STEM knowledge is not always accessible to primary classroom teachers, nor is it always obvious how to implement such authentic problems within a classroom context. In this exploratory case study, we present data from a Year 4 classroom (age 9) statistical investigation addressing, 'How big is a leaf?' linking data to the real-life STEM context they represented. The authors were interested in how the teacher's communication processes supported her students' emerging understandings about data. Wit's (2018) cognitive tuning framework offered a way to capture how the communication processes in a group build to a commonly shared frame of reference. Findings revealed a pattern of communication between the teacher and students, supporting students' changing conceptions of data and related statistical thinking processes, throughout the investigation. [ABSTRACT FROM AUTHOR]

The reciprocal relationship between psychiatric and substance use disorders is well-known, yet it remains largely unknown whether mental health morbidity causally leads to addictive behaviours. This paper utilises a fixed effects instrumental variables model, which is identified by time-varying sources of plausibly exogenous variations in mental health, and a nationally representative panel dataset from Australia to present robust evidence on the causal impact of mental distress on cigarette smoking and alcohol drinking behaviours. We find that mental distress significantly increases the prevalence and intensity of either cigarette or alcohol consumption. Further analysis reveals that mental distress also substantially increases household monetary expenditures on either tobacco or alcohol. The impact is greater for lower educated individuals or children of smokers, and is slightly higher for males. Our findings highlight the importance of mental health screening and treatment programs, especially among lower educated individuals or children of smokers, to assist in the prevention of addictive activities. [ABSTRACT FROM AUTHOR]

The transformation of cities into smarter and more efficient environments relies on proactive and timely detection and maintenance of city-wide infrastructure, including roadside infrastructure such as road signs and the cleaning of illegally dumped rubbish. Currently, these maintenance tasks rely predominantly on citizen reports or on-site checks by council staff. However, this approach has been shown to be time-consuming and highly costly, resulting in significant delays that negatively impact communities. This paper presents AIoT-CitySense, an AI and IoT-driven city-scale sensing framework, developed and piloted in collaboration with a local government in Australia. AIoT-CitySense has been designed to address the unique requirements of roadside infrastructure maintenance within the local government municipality. A tailored solution of AIoT-CitySense has been deployed on existing waste service trucks that cover a road network of approximately 100 kms in the municipality. Our analysis shows that proactive detection for roadside infrastructure maintenance using our solution reached an impressive 85%, surpassing the timeframes associated with manual reporting processes. AIoT-CitySense can potentially transform various domains, such as efficient detection of potholes and precise line marking for pedestrians. This paper exemplifies the power of leveraging city-wide data using AI and IoT technologies to drive tangible changes and improve the quality of city life. [ABSTRACT FROM AUTHOR]

The COVID-19 virus was unveiled to the world as a health crisis and later also as an economic crisis. For some organisations, it has become an ethical crisis. This is certainly the case for large organisations in Australia, where the way many enterprises handled a government wage subsidy called JobKeeper led to a public backlash, media pressure, and a variety of responses ranging from 'We acted legally' to the full return of the subsidy. Some organisations later reported profits, and the public response indicated concern about this behaviour, many considering it immoral despite it being legally compliant. It is, we contend, a question to which stakeholder theory can be applied, examining how organisations view and respond to the public. We use content analysis of mainstream media to provide information about public reactions and information from official sources to confirm corporate action. We show that there is a significant ethical component in the public response to the behaviour of organisations as they respond to the crisis. COVID has been an ethical, health, and financial crisis for these organisations. Public pressure, exerted in and through the media, made the general public a definite stakeholder. [ABSTRACT FROM AUTHOR]

Understanding the drivers of student dropout from higher education has been a policy concern for several decades. However, the contributing role of certain factors—including student mental health—remains poorly understood. Furthermore, existing studies linking student mental health and university dropout are limited in both methodology and scope—for example, they often rely on small and/or non-representative samples or subjective measures, and focus almost exclusively on main effects. This paper overcomes many of these shortcomings by leveraging unique linked administrative data on the full population of domestic students commencing undergraduate studies at Australian universities between 2012 and 2015 (n = 652,139). Using these data, we document that approximately 15% of students drop out of university within their first academic year. Critically, students receiving treatment for mental health problems are 4.3 (adjusted) to 8.3 (unadjusted) percentage points more likely to drop out of higher education. This association remains in the presence of an encompassing set of potential confounds, and is remarkably uniform across segments of the student population determined by individual, family, and programme characteristics. Altogether, our findings call for increased policy efforts to improve student mental health and to buffer against its deleterious effects on retention. [ABSTRACT FROM AUTHOR]