Monash University Research Portal
Not a member yet
41460 research outputs found
Sort by
The divine in the clinic:assisted reproduction and religious practice in Ghana and South Africa
Get PDFDrawing on studies with 40 informants in Ghana and 74 informants in South Africa, we explore spiritual interventions among staff and patients that accompany their use of assisted reproduction. These practices and expressions of faith reinforce staff and patients as moral subjects who have done everything possible to assist in the vagaries of assisted reproduction—another form of care to enable, complement, and enhance high-tech intervention. We consider the creation of sacred spaces in the clinics, the rituals that form part of IVF practice, and the dilemmas of translation when assisted reproductive technologies (ARTs) travel to different cultural and religious contexts.</p
The testimony gap:machines and reasons
No full textMost people who have considered the matter have concluded that machines cannot be moral agents. Responsibility for acting on the outputs of machines must always rest with a human being. A key problem for the ethical use of AI, then, is to ensure that it does not block the attribution of responsibility to humans or lead to individuals being unfairly held responsible for things over which they had no control. This is the “responsibility gap”. In this paper, we argue that the claim that machines cannot be held responsible for their actions has unacknowledged implications for the conditions under which the outputs of AI can serve as reasons for belief. Following Robert Brandom, we argue that, because the assertion of a claim is an action, moral agency is a necessary condition for the giving and evaluating of reasons in discourse. Thus, the same considerations that suggest that machines cannot be held responsible for their actions suggest that they cannot be held to account for the epistemic value — or lack of value — of their outputs. If there is a responsibility gap, there is also a “testimony gap.” An under-recognised problem with the use of AI, then, is to ensure that it does not block the attribution of testimony to human beings or lead to individuals being held responsible for claims that they have not asserted. More generally, the “assertions” of machines are only capable of serving as justifications for belief or action where one or more people accept responsibility for them.</p
Developing and assessing MyTBCompanion – A tri-lingual integrated video observed therapy app for tuberculosis patient management in Malaysia and Indonesia
No full textLimited studies have developed a mobile phone-based application that supports Asynchronous Video Observed Therapy (A-VOT) for Tuberculosis (TB) program. This study aimed to design, develop, and assess MyTBCompanion, a mobile phone-based digital health intervention to support A-VOT for TB management and care among low-income patients in Malaysia and Indonesia. Methods MyTBCompanion was designed and developed under a partnership involving experts in information technology, respiratory disease, and linguistics. Pre and Post-test surveys were done to assess feedback on the existing TB strategies (Direct Observed Therapy (DOT) and/or VOT) and the usability of the tested A-VOT strategy using MyTBCompanion. We collected data on the patient’s age, education level, current treatment strategy, and statements measuring four MyTBCompanion components: Engagement (how interesting, customizable, interactive and well-targeted to an audience an app is), Functionality (ease of use, app design, navigation), Aesthetic (the graphic design, overall visual appeal, color scheme, and stylistic consistency) and Information (content accuracy and relevance). The Mann-Whitney U test was performed for group comparisons, with results considered significant at p < 0.05. Results In total, 49 patients with TB were recruited from Malaysia (n = 29) and Indonesia (n = 20). Most participants in both countries were 20–40 years old. Indonesian participants mainly had tertiary education (11/20, 55.0%), whereas most had secondary education level in Malaysia (17/29, 58.6%). Most Malaysian participants (19/29, 65.5%) were using VOT through WhatsApp, with fewer (10/29, 34.5%) using DOT. All participants from Indonesia (20/20, 100%) were using the DOT strategy. Overall, compared to the existing strategies, a higher mean agreement score was observed for MyTBCompanion, with Information scoring the highest agreement (4.57/5.0), followed by Engagement (4.53/5.0), Functionality (4.51/5.0) and Aesthetic (4.49/5.0). Conclusion Findings suggest an overall good agreement on the usability of A-VOT strategy using MyTBCompanion in terms of engagement, functionality, information and aesthetics, with many indicating their willingness to recommend it to others, marking an encouraging milestone in the app’s development.</p
Peer navigation for clients on addiction treatment waiting lists
Get PDFPeer navigation has potential to overcome barriers and improve skills to enhance recovery. We piloted peer navigation with clients on Turning Point’s Eastern Treatment Services’ waiting list. Eligible clients (n=25) were referred to a peer worker, for recovery capital (REC-CAP) baseline assessment and goal setting, and again at 4- and 12-weeks. Of 25 participants (55% male), mean age was 44 and the primary drug of concern for most (n=18; 72%) was alcohol. Goals included attending recovery groups, engaging with services, and improving relationships. Qualitative findings indicate benefits of navigation, including readiness for treatment. This study builds on our program of research on how peer navigation can improve recovery skills and connection. Peer workers can play an important role in a waiting list setting, and results support investing in a larger trial
Factors and challenges of withdrawal from postgraduate studies:interviews with Chinese international students
Get PDFAn increasing number of Chinese postgraduate students, both in China and internationally, are sharing on social media their experiences of withdrawing from their studies. However, there is little research examining the factors associated with their withdrawal. This project aimed to examine why and how Chinese international students in Australia withdrew or considered withdrawing from their postgraduate programs
Net Zero Precincts:Stage 3: Activating
Get PDFIn this report we share details of the Living Lab that was developed to accelerate net zero precinct transitions across domains including energy, mobility, buildings, governance and data and build capability for a new style of collaborative and place-based transition governance. This report presents an overview of the Living Lab’s structure, cycles of engagement, theory of change and the monitoring and evaluation approach that was used to support reflexive learning. The Living Lab grew to 10 teams through a portfolio of experiments that ran over the course of 2024 and 2025. The Transformative Outcomes approach was used to enable experiment leads to re-calibrate their experiments and harness appropriate leverage points to promote learning, networking, navigate expectations, institutional change and actor empowerment. This report shows researchers, policymakers and practitioners how diverse actors can be brought together through a Living Lab using a portfolio-based approach to trial experiments that generate transformative outcomes to drive systems change in precinct settings
Planetary health:increasingly embraced but not yet fully realised
Get PDFThe modern field of 'planetary health' was instigated in 2015 by the Rockefeller Foundation-Lancet Commission, which defined it as 'the health of human civilisation and the state of the natural systems on which it depends'. However, this view of human health in relation to natural systems is not really new at all. Rather, it is (re)emerging as the environmental impacts of human activities and their effects on the health of all life on Earth, now and in the future, become increasingly clear. A planetary health approach requires us to rethink dominant perspectives about how we feed, move, house, power and care for the world, as well as the implications for wellbeing and equity across generations and locations. This shift in understanding of our place as humans in relation to the planet is fundamental to addressing the polycrises of the 21st century. Planetary health approaches are increasingly embraced but not yet fully realised or embedded. More organisations and collaborations, in the health sector and beyond, are incorporating these ideas into their methods, plans and training, including concepts that are part of, but not synonymous with planetary health, such as one health, global health, environmental health, climate health and sustainable healthcare. Yet, we are still far from the collective cultural transformation needed to achieve the promise of planetary health as a movement that puts the health of people and the planet at the centre of all policy and action. Education and training in the Western tradition encourage 'human-centred' or 'colonial' thinking. There is much to (re)learn from First Nations peoples, and other non-Western worldviews, about the interdependence of all species and what that means for sustainable health and wellbeing. We offer proposals for how public health policymakers, researchers and practitioners, might support the transformation needed and address the conceptual, knowledge and governance challenges identified by the Rockefeller Foundation-Lancet Commission.</p
Qini curves for multi-armed treatment rules
No full textQini curves have emerged as an attractive and popular approach for evaluating the benefit of data-driven targeting rules for treatment allocation. We propose a generalization of the Qini curve to multiple costly treatment arms that quantifies the value of optimally selecting among both units and treatment arms at different budget levels. We develop an efficient algorithm for computing these curves and propose bootstrap-based confidence intervals that are exact in large samples for any point on the curve. These confidence intervals can be used to conduct hypothesis tests comparing the value of treatment targeting using an optimal combination of arms with using just a subset of arms, or with a non-targeting assignment rule ignoring covariates, at different budget levels. We demonstrate the statistical performance in a simulation experiment and an application to treatment targeting for election turnout.</p
What generative Artificial Intelligence priorities and challenges do senior Australian educational policy makers identify (and why)?
No full textFree access to powerful generative Artificial Intelligence (AI) in schools has left educators and system leaders grappling with how to responsibly respond to the consequent challenges and opportunities that this new technology poses. This paper examines the priorities and challenges that senior Australian educational leaders identify with relation to responsible and ethical use of generative AI in school education, and the reasons for their beliefs. Members of the Australian generative Artificial Intelligence in Education working group as well as other senior policymakers throughout Australia participated in a two-phase data collection process involving survey responses and focus group discussions. Ranking activities revealed a large number of priorities and systemic challenges, with no unilateral consensuses emerging. The highest priorities for senior policymakers related to managing risks, educating teachers, and educating system leaders, while the main systemic and environmental challenges related to the pace of change, teacher capabilities and professional learning, and equitable access to the technology. Throughout the analysis, meta themes emerged that characterised the policy-setting environment as one involving urgency, uncertainty, interconnectedness, contextuality, and complexity, with the pivotal role of teachers highlighted throughout. Reflections on responsible and ethical policy-setting in response to rapid technological change are provided, including with relation to anticipatory and networked governance and the inter-relationship with the broader policy context. Recommendations for further research and practice are also proposed.</p
Multi-task AI models in dermatology:Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis
No full textBackground: The surge in AI models for diagnosing skin lesions through image analysis is notable, yet their clinical implementation faces challenges. Common limitations include an over reliance on dermoscopy, lack of real-world applicability when only binary output (e.g. benign/malignant) is offered and low accuracy when faced with rare skin conditions. Objective: To address these common constraints associated with limited diagnostic output, and applicability to real-world settings. Methods: We developed an All-In-One Hierarchical-Out of Distribution-Clinical Triage (HOT) AI model for skin lesion analysis. Trained on a large dataset of ~208,000 lesion images, our HOT AI model generates three outputs: a hierarchical three-level prediction, an alert for out-of-distribution (OOD) images and a recommendation for dermoscopy to improve diagnostic prediction. Results: Our hierarchical prediction output provides a binary level 1 prediction (benign/malignant), Level 2 prediction of eight possible categories (e.g. melanocytic and keratinocytic) and a more definitive Level 3 prediction from 44 lesion categories. The model produced high sensitivity for Level 1 prediction (88.14% CI: 87.42–88.51); however, significantly lower for Level 3 prediction (63.90%, CI: 62.27–65.61). By relying on all three prediction levels for consensus, Level 1 false-positives were reduced by 20–25%, and false-negatives were decreased by 11–13% of cases. OOD detection was benchmarked against previous landmark models and outperformed comparative models. Lastly, 44% of images were recommended for dermoscopy, and with additional image input, Level 3 sensitivity increased from 48.13% (CI:45.08–49.57) to 52.54% (CI:50.25–55.04). Conclusion: Our HOT-AI model attempts to address common challenges in existing models by combining three tasks in one model to increase accuracy and clinical utility. By providing a more nuanced prediction, and alert for OOD, the model output provides greater explainability of the AI decision process. Prospective clinical testing is required to measure how this additional output impacts user trust, and how the model performs in a real-world setting.</p