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Pride and persistence:social comparisons in production
No full textWork is ordinary and necessary for most people, but some people work excessively (“work persistence”), seemingly driven by internal forces. We theoretically and experimentally investigate the role of relative performance incentives in causing or exacerbating work persistence. In our setting, agents perform a task over two stages. In the first stage, they can earn prizes, which are allocated either randomly or according to relative performance. Afterwards, they have the opportunity to continue working in a second stage, with payment by piece rate and no competition against others. Our theoretical model of motivated belief updating predicts that agents adjust their beliefs asymmetrically: they attribute their relative performance more to their productivity if they win a prize, and more to luck if they lose. This bias leads winners of the first-stage prize to increase their effort in the subsequent piece-rate stage, but with no corresponding decrease in work effort by losers. Results from a real-effort experiment confirm these predictions: winners' effort in the piece-rate stage is roughly 30 percent higher when earlier bonus prizes had been allocated by performance, compared to when those prizes had been allocated randomly. Losers' effort is also higher – not lower – though this difference is not significant.</p
Co-Producing Patient-Reported Experience Measures With People With Intellectual Disability to Improve Healthcare Quality and Outcomes:The ‘Listen to Me’ Project Protocol
No full textIntroduction: Intellectual disability, defined by significant limitations in both intellectual functioning and adaptive behaviour with onset during the developmental period, affects an estimated 2% (108 million) of people worldwide. People with intellectual disability experience major health inequity, poor health outcomes and premature deaths, with mortality rates that are 7–12 times higher than the general population. Patient-reported experience measures (PREMs) are used worldwide to target improvements in healthcare delivery. Yet, systematic review evidence confirms that people with intellectual disability are excluded from PREMs due to a lack of suitable measurement instruments and supports. To improve healthcare quality and outcomes, people with intellectual disability and their supporters, and academic and clinician researchers will together Coproduce PREMs for, and with, this population apply the PREM in hospitals and use the data to inform local quality improvement projects. Methods and Analysis: Our 3-year project employs a multi-method design using coproduction, underpinned by an implementation science framework. We will coproduce PREMs that will be applied to improve healthcare quality and outcomes for people with intellectual disability whilst engaging in co-research with people with lived experience of having an intellectual disability and their carers, clinicians and academic researchers. Study 1 will coproduce PREMs for use by people with intellectual disability to report their experiences of inpatient hospital care. Study 2 will use the co-produced PREMs to capture the experiences of people with intellectual disability as inpatients in Australian hospitals; determine PREMs reliability and validity; and costs associated with use. Study 3 will develop the required capability to translate our PREMs into hospitals across Australia. Study 4 will apply PREMs data via continuous quality improvement in partnering hospitals to reduce preventable healthcare associated harm, hospitalisation, and prolonged length of stay experienced by people with intellectual disability. Study 5 will provide a process evaluation of our co-research approach. Quantitative and qualitative analysis will be undertaken, and results will be examined against the project aims. Ethics and Dissemination: Ethical approval has been obtained (520241735259588; X24-0366; 115771). This study is being conducted with partner health agencies and services nationally in Australia to support achievement of the research aims and translation of findings into practice. Targeted outputs with research dissemination will be guided by, and in collaboration with, the project Consumer Leadership Group, consumer and health system stakeholders, with governance from the Project Steering Group. Patient or Public Contribution: The Listen to Me project has been designed and planned to ensure the involvement of people with a broad range of abilities, including those with profound intellectual disability who will be able to contribute along with their support person/s. The governance structure of Listen to Me is innovative and inclusive, with consumers providing leadership across all elements of the project. This is co-research conceptualised and conducted with a Consumer Leadership Group (CLG) comprising eight people with lived experiences of intellectual disability; two of whom have an intellectual disability and six of whom are parents or siblings who support family members with intellectual disability. The CLG were involved in the design of the research proposal, reviewing and contributing to the ethics protocols, and the writing of this protocol as authors. The CLG will direct and contribute to all aspects of the research as part of the research team. An easy read summary of this protocol is provided in Supplementary file 1.</p
Spatial resolution impact on public transport accessibility measurement error
No full textPlace-based accessibility measures are critical to transport planning. However, their accuracy can vary greatly depending on the spatial resolution of representative points used in calculations. This study refers to this precision issue as measurement resolution error, and its impacts have not yet been comprehensively explored in public transport accessibility analysis. Using Melbourne, Australia, as the study area, this study examined measurement resolution error across various spatial resolutions, public transport service levels, opportunity densities, types of accessibility measures and time thresholds. Results reveal that while the errors are generally small, their magnitude is highly context dependent. Smaller time thresholds, lower service levels, opportunity density that leads to lower accessibility values, and more complex accessibility measures all increase sensitivity to this error. Suburban areas with sparsely populated mixed land use that are served with modest public transport services are the most vulnerable to measurement resolution error, creating significant transport equity implications. The study recommends the use of data at resolutions that can reflect land use information for general public transport accessibility analysis, while building-level resolution data should be selectively applied if analysis objectives warrant such granularity. These findings highlight the importance of being aware of measurement resolution errors and provide evidence-based guidance for balancing analytical precision with computational efficiency in transport planning.</p
Graph-Regularized Manifold-Aware Conditional Wasserstein GAN for Brain Functional Connectivity Generation
No full textCommon measures of brain functional connectivity (FC) including covariance and correlation matrices are symmetry-positive definite (SPD) matrices residing on a cone-shaped Riemannian manifold. Despite its remarkable success for Euclidean-valued data generation, the use of standard generative adversarial networks (GANs) to generate manifold-valued FC data neglects its inherent SPD structure and hence the inter-relatedness of edges in real FC. We propose a novel graph-regularized manifold-aware conditional Wasserstein GAN (GR-SPD-GAN) for FC data generation on the SPD manifold that can preserve the global FC structure. Specifically, we optimize a generalized Wasserstein distance between the real and generated SPD data under adversarial training, conditioned on the class labels. The resulting generator can synthesize new SPD-valued FC matrices associated with different classes of brain networks, for example, brain disorder or healthy control. Furthermore, we introduce additional population graph-based regularization terms on both the SPD manifold and its tangent space to encourage the generator to respect the inter-subject similarity of FC patterns in the real data. This also helps in avoiding mode collapse and produces more stable GAN training. Evaluated on resting-state functional magnetic resonance imaging (fMRI) data of major depressive disorder (MDD), qualitative and quantitative results show that the proposed GR-SPD-GAN clearly outperforms several state-of-the-art GANs in generating more realistic fMRI-based FC samples. When applied to FC data augmentation for MDD identification, classification models trained on augmented data generated by our approach achieved the largest margin of improvement in classification accuracy among the competing GANs over baselines without data augmentation.</p
Unravelling public preferences for the use of Artificial Intelligence mobile health applications in Australia
No full textObjectives: To explore public opinion on the factors that drive the use of artificial intelligence (AI) mobile health (mHealth) applications for heart disease and mental health, with a particular emphasis on diagnostics and virtual health assistance (VHA). Methods: This study adopted a discrete choice experiment to investigate the preferences of the Australian general public for heart disease and depression. A total of 5 attributes were considered, including anonymized data sharing, human-AI interaction, accuracy of AI results, explanation of results provided by AI, and funding source. Mixed logit and latent class logit models were used to investigate potential preference heterogeneity among respondents. Results: Respondents (n = 1176) showed that AI accuracy was the most crucial factor in AI mHealth applications, followed by human doctor-AI interaction. Preferences for not sharing anonymized data were reported in depression, whereas there were no statistically significant results for heart disease. Results explained by AI and funding source were generally less important. Those who expressed fear of AI were less likely to opt for AI diagnostics and VHA in heart disease. Older adults (60+) were less likely to use AI in both health conditions, whereas younger adults (18-29) were more inclined to use VHA for heart disease. Conclusions: It is evident that beyond the technical feasibility of AI applications, there are nuanced differences in public preferences for AI mHealth applications in Australia. Understanding factors leading to these discrepancies would be valuable for ensuring safe and equitable acceptance and harnessing the full potential of AI in healthcare delivery and outcomes.</p
SAFE:A Novel Approach For Software Vulnerability Detection from Enhancing The Capability of Large Language Models
No full textSoftware vulnerabilities (SVs) have emerged as a prevalent and crucial concern for safety-critical systems. This has spurred significant advancements in utilizing AI-based methods, including machine learning and deep learning, for software vulnerability detection (SVD). While AI-based methods have shown promising performance in SVD, their effectiveness on real-world, complex, and diverse source code datasets remains limited in practice. To tackle this challenge, in this paper, we propose a novel framework that enhances the capability of large language models to learn and utilize semantic and syntactic relationships from source code data for SVD. As a result, our proposed SAFE approach can enable the acquisition of fundamental knowledge from source code data while adeptly utilizing crucial relationships, i.e., semantic and syntactic associations, to improve the effectiveness of solving the SVD problem. The rigorous and extensive experimental results on three real-world challenging datasets (i.e., Devign, ReVeal, and D2A) demonstrate the superiority of our approach over eight effective and state-of-the-art baselines. In summary, on average, our SAFE approach achieves higher performances from 4.79% to 11.57% for F1-measure and from 16.93% to 26.24% for Recall compared to the baseline methods across all the datasets used.</p
Universally Composable Traceable Ring Signature with Verifiable Random Function in Logarithmic Size
No full textTraceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.</p
Alignment between top-down disaster indices and local views on disaster preparedness
No full textThis study examines whether top-down indices of disaster resilience and vulnerability align with individuals' perceptions of preparedness and coping capacity. Using data from a nationally representative survey of Australians in 2021, we match individuals' self-reported perceptions to two indices: the Vulnerability Index and the Australian Natural Disaster Resilience Index. Regression analyses reveal that these indices, including most of their sub-components, are weakly or inconsistently associated with perceived preparedness and coping capacity. These patterns persist across demographic groups and for individuals with recent disaster experience. The misalignment appears to stem partly from the indices’ strong correlation with area-level socioeconomic status. Although socioeconomic advantage is typically assumed to improve resilience and reduce vulnerability, we find it is not a strong predictor of how prepared or capable people feel. These findings raise questions about how resilience and vulnerability are measured and interpreted, particularly when used to guide policy and funding decisions. We argue that top-down indices and local perceptions capture different dimensions of resilience, and using both in parallel could improve the targeting and effectiveness of resilience-building strategies.</p
Financial literacy and clean energy adoption in South Africa
No full textWe examine the causal effect of financial literacy on clean energy adoption in South Africa. Our main estimates, in which we instrument for financial literacy with mathematics education received, suggest that a unit increase in financial literacy causes a 14.1 percentage point increase in the likelihood of adopting clean energy for cooking, heating or lighting. The magnitude of the size effect when clean lighting use is considered separately is similar to the composite measure, while the effect sizes for clean cooking and heating are much larger. The heterogeneity results suggest that the effect of financial literacy on clean energy adoption is larger in households with a male household head and in households located in rural areas. These findings are robust to alternative estimation methods, alternative identification methods that do not rely on an external instrument and ways of measuring financial literacy. We find that entrepreneurship, financial inclusion and generalized trust are channels through which financial literacy influences clean energy adoption. We conclude by suggesting policies for promoting financial literacy and each of the channels through which financial literacy affects clean energy use to enable households to make sustainable energy choices.</p