13463 research outputs found
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Catch 22: Institutional ethics and researcher welfare within online extremism and terrorism research
Get PDFDrawing from interviews with 39 online extremism and terrorism researchers, this article provides an empirical analysis of these researchers’ experiences with institutional ethics processes. Discussed are the harms that these researchers face in the course of their work, including trolling, doxing, and mental and emotional trauma arising from exposure to terrorist content, which highlight the need for an emphasis on researcher welfare. We find that researcher welfare is a neglected aspect of ethics review processes however, with most interviewees not required to gain ethics approval for their research resulting in very little attention to researcher welfare issues. Interviewees were frustrated with ethics processes, indicating that committees oftentimes lacked the requisite knowledge to make informed ethical decisions. Highlighted by interviewees too was a concern that greater emphasis on researcher welfare could result in blockages to their ‘risky’ research, creating a ‘Catch 22’: interviewees would like more emphasis on their (and colleagues’) welfare and provision of concomitant supports, but feel that increased oversight would make gaining ethics approval for their research more difficult, or even impossible. We offer suggestions for breaking the impasse, including more interactions between ethics committees and researchers; development of tailored guidelines; and more case studies reflecting on ethics processes
Explainable AI for Infection Prevention and Control: Modeling CPE Acquisition and Patient Outcomes in an Irish Hospital with Transformers
No full textCarbapenemase-Producing Enterobacteriace poses a critical concern for infection prevention and control in hospitals. However, predictive modeling of previously highlighted CPE-associated risks such as readmission, mortality, and extended length of stay (LOS) remains underexplored, particularly with modern deep learning approaches. This study introduces an eXplainable AI modeling framework to investigate CPE impact on patient outcomes from Electronic Medical Records data of an Irish hospital. We analyzed an inpatient dataset from an Irish acute hospital, incorporating diagnostic codes, ward transitions, patient demographics, infection-related variables and contact network features. Several Transformer-based architectures were benchmarked alongside traditional machine learning models. Clinical outcomes were predicted, and XAI techniques were applied to interpret model decisions. Our framework successfully demonstrated the utility of Transformer-based models, with TabTransformer consistently outperforming baselines across multiple clinical prediction tasks, especially for CPE acquisition (AUROC and sensitivity). We found infection-related features, including historical hospital exposure, admission context, and network centrality measures, to be highly influential in predicting patient outcomes and CPE acquisition risk. Explainability analyses revealed that features like "Area of Residence", "Admission Ward" and prior admissions are key risk factors. Network variables like "Ward PageRank" also ranked highly, reflecting the potential value of structural exposure information. This study presents a robust and explainable AI framework for analyzing complex EMR data to identify key risk factors and predict CPE-related outcomes. Our findings underscore the superior performance of the Transformer models and highlight the importance of diverse clinical and network features
Guerre et Constitution : Irlande
No full textL’adoption de la Constitution irlandaise en 1937 intervient après deux
guerres, la guerre d’indépendance et la guerre civile. Constitution républicaine la plus ancienne d’Europe, Bunreacht na hÉireann a servi de cadre pendant les conflits du vingtième siècle, la seconde guerre mondiale sur la scène internationale et la guerre contre le terrorisme en Irlande du Nord sur la scène interne. Ces circonstances historiques et politiques particulières expliquent pourquoi l’Irlande est devenue un pays neutre avec, aujourd’hui, une armée de taille très modeste. Ce chapitre a pour objet d’expliquer le rapport de la Constitution irlandaise à la guerre selon les deux axes suivants : en premier lieu, une approche historico-constitutionnelle qui expose les interactions entre guerre et constitution; en second lieu, une approche politico-institutionnelle qui explique les principes de gestion de la guerre par la Constitution
Artificial Intelligence Techniques and Health Literacy: A Systematic Review
No full textObjective: To systematically review the utilization of artificial intelligence (AI) in health literacy, highlighting limitations and future developments.
Methods: A systematic review, following PRISMA guidelines, was conducted searching 6 databases for studies published from January 1, 2014, through April 10, 2024. Data extracted included population
characteristics, health literacy definitions and measurement, study objectives, AI techniques, and metrics. Risk of bias was assessed using an adapted checklist. Results: From 1296 studies, 18 (1.4%) met inclusion criteria. These studies primarily evaluated textbased materials, including online articles, and electronic health records, with most materials in English, but also incorporated other languages. Artificial intelligence played various roles, including evaluating complexity, text simplification/readability enhancement, translation, and question-answering. Only 5 studies involved participant engagement. Seven studies provided a health literacy definition, consistently describing it as an individual’s ability to obtain, understand, and use health information for
informed decisions, often linking it to external factors. However, only 1 study incorporated an individual level health literacy measurement tool, whereas organizational level health literacy measurement remained largely overlooked. The AI techniques used included traditional machine learning, deep learning, and transformer-based models. Evaluation metrics were categorized into human evaluation, readability, and machine learning metrics. Conclusion: The review highlights AI’s dynamic application in relation to health literacy; however, measurement of health literacy, at both an individual and organizational level, to evidence AI’s effectiveness remains limited. In addition, future work should not only measure health literacy outcomes more rigorously but also pursue research on enhancing AI model performance, robust evaluation, and
their practical implementation in real-world settings
Widespread yet unreliable: Systematic analysis of the presence studies that employ questionnaires
No full textPresence, as a psychological state, is typically assessed using questionnaires. While many researchers in this field assume that these
self-report instruments are standardized, the reliability of such questionnaires remains uncertain. This knowledge gap challenges the
accuracy and validity of data derived from studies assessing presence. Ensuring reliable and precise data collection and reporting
is essential for the credibility of findings in presence research, because inaccuracies may cause errors in conclusions, which affects
theoretical understandings, methodological approaches and practical applications. To address this issue, we conducted a systematic
analysis of 397 empirical quantitative studies on presence. We investigated the use of presence scales, including applications,
modifications, a variety of measures and reporting practices.We found that the majority of the presence studies modify questionnaires,
do not re-validate them and improperly report their methods. Based on these findings, we propose solutions to enhance transparency
and validation of the presence measurements
“It’s wishy-washy [...] You are getting this diagnosis because we’ve ruled out everything else.” Developmental language disorder (DLD) diagnosis in the Republic of Ireland: A qualitative exploration of the perspectives of parents and clinicians
No full textDevelopmental Language Disorder (DLD) affects around 7% of children globally, yet the scholarship on it is significantly underdeveloped. Emerging research suggests that DLD is both underdiagnosed by clinicians and misunderstood by parents. This study explores the perspectives of both clinicians and parents regarding their experiences of the process of giving and receiving a DLD diagnosis in the Republic of
Ireland. Semi-structured qualitative group interviews were conducted with 15 parents and seven clinicians. The data were analysed using a reflexive, thematic analysis. Four themes were identified: “challenges of giving a DLD diagnosis”, “communicating a DLD diagnosis”, “utility of DLD diagnosis for children and families” and “going forward and recommendations”. Clinicians reported systemic barriers in the healthcare system, including limited therapy time, long waitlists, and staff turnover, as major challenges in diagnosing DLD. Many expressed a lack of confidence in providing a diagnosis without multidisciplinary support which would support them in ‘ruling out’ other neurodevelopmental differences. Communicating the diagnosis was often inconsistent, with many parents feeling unsupported and uninformed about the nature
and impact of DLD. Parents felt inadequate at being left with communicating the diagnosis of DLD and its impact to their children. The participants emphasised urgent need for greater awareness, teacher and clinical education, post-diagnosis support, and increased national advocacy in relation to DLD in Ireland. Both clinicians and
parents saw DLD diagnosis as essential for accessing therapeutic and educational support, yet it was the access to these supports that seemed to influence the diagnostic decisions. Importantly, our research documents clinicians’ fear of getting the “right” condition for diagnosis, which may be firstly at odds with the individual profiles of children, and secondly, acts as a barrier in accessing the needed support. In light
of growing awareness of the co-occurrence of neurodevelopmental differences, we call for enhanced support for clinicians to build their confidence in navigating the evolving diagnostic criteria of DLD
Living Labs as Democratic Infrastructure: Co-producing Education Policy through Participatory Research
No full textThis paper examines how Living Lab methodologies, developed through collaborative research–practice–policy partnerships in Ireland and Europe, can support the ambitions of the National Education Convention. It presents Living Labs as a democratic infrastructure for co-producing education policy and embedding learner and teacher agency within reform processes. Drawing on Irish case studies and DCU’s participatory research with the Department of Education, the paper outlines how Living Labs enable inclusive policymaking, iterative feedback between practice and policy, and scalable models for system change. Informed by participatory democracy and design-based implementation research, the findings highlight enhanced democratic competences, educator professionalism, and community inclusion. Living Labs are proposed as a sustainable, future-focused mechanism for bridging research, policy, and practice, offering a model for more democratic, resilient, and context-responsive education systems in Ireland
Can Structured Literacy Be a New Dimension for Interprofessional Practice Between Teachers and SLTs? Perceptions of Irish SLTs on Their Capacity and Practices in Supporting Children With Literacy Difficulties
No full textBackground: The role of speech and language therapists (SLTs) in supporting literacy in Ireland is especially timely to consider
given the expansion of multi-tiered systems of support and the increased provision of structured literacy instruction in schools.
To advance SLT–teacher collaboration in literacy, we must first explore Irish SLTs’ perspectives. Do they perceive themselves as
having the required skills and confidence to support both children with literacy difficulties and the teachers who work with them?
Aims: This study aimed to explore Irish SLTs’ current practice and confidence in supporting literacy, as well as their readiness to
collaborate with teachers to enhance children’s literacy outcomes.
Methods and Procedures: Participants were members of the Irish Association of Speech and Language Therapists’ Special
Interest Group (SIG) in Developmental Language Disorder. Thirty-five SIG members completed an anonymous online survey,
adapted from previous questionnaires, designed to explore SLTs’ literacy practices, perceived scope of practice and confidence
across different literacy domains. The survey also examined participants’ engagement in consultative models of service provision.
Outcomes and Results: Most SLTs felt that supporting children with literacy difficulties fell within their scope of practice;
however, only a minority reported overall confidence to work within the literacy domain. Confidence varied across distinct areas
of literacy, with participants reporting strong confidence in phonological awareness, vocabulary and morphology - key areas of
structured literacy in which teachers often need guidance. In contrast, they reported low confidence in supporting spelling, which
a majority of SLTs considered outside of their remit. The findings show clear support among SLTs for the consultative model of
service provision, both in terms of its value and feasibility, yet most participants did not include literacy in their consultative work.
Overall, there was no clear consensus about the potential contributions SLTs could make to supporting literacy instruction or the
multi-tiered systems of support model in schools. Conclusions and Implications: These findings highlight the need for interprofessional education (IE) initiatives for prospective teachers and SLTs to enhance multi-tiered systems of literacy support in schools. Structured literacy offers a focussed, equitable domain for such collaboration. Future research could explore SLT-teacher partnerships and the development of research scholarship in this area. Strengthening SLTs’ role in structured literacy in Ireland could provide a meaningful avenue for
interprofessional practice and improve literacy outcomes for children
Representations of foreign nationals in Japanese disaster risk reduction policy: increasing alignment with a “whole community” discourse
No full textPurpose – This article examines how foreign nationals have been represented in the disaster risk reduction
(DRR) policy discourse in Japan in recent decades and analysesthe consequencesthese representations have had
for foreign nationals’ DRR there.
Design/methodology/approach – The article reports on a monolingual, corpus-based, critical discourse
analysis of 23 years of White Papers on Disaster Management in Japan to assessthe discourse representations of
foreign nationals in the policy texts over three chronological periods: 2001–2008, 2009–2016 and 2017–2023.
Findings – The article findsthat the way the Government ofJapan has communicated to stakeholders about foreign
nationals through its policy has increasingly included them into a “whole community” discourse of DRR. This is
significant because research hasshown thatforeign nationals have long gone un- or under-recognised in relevant DRR
policies and have been insufficiently considered at local levels. If they are now better represented in policy as local
community members, there is hope that their risk of negative consequencesin times of disaster will truly be reduced.
Originality/value – This contribution is novel in that it addresses a nexus between discourse, policy
communication and a social problem of inclusion and engagement of foreign nationalsin DRR that has not been
published elsewhere, but that nonetheless engages with ongoing academic conversations about inclusivity,
vulnerability and community-based DRR approache
Enhancing Bagging Ensemble Regression with Data Integration for Time Series-Based Diabetes Prediction
No full textDiabetes is a chronic metabolic disease characterized by elevated blood glucose levels, leading to complications like heart disease, kidney failure, and nerve damage. Accurate state-level predictions are vital for effective healthcare planning and targeted interventions, but in many cases, data for necessary analyses are incomplete. This study begins with a data engineering process to integrate diabetes-related datasets from 2011 to 2021 to create a comprehensive feature set. We then introduce an enhanced bagging ensemble regression model (EBMBag+) for time series forecasting to predict diabetes prevalence across U.S. cities. Several baseline models, including SVMReg, BDTree, LSBoost, NN, LSTM, and ERMBag, were evaluated for comparison with our EBMBag+ algorithm. The experimental results demonstrate that EBMBag+ achieved the best performance, with an MAE of 0.41, RMSE of 0.53, MAPE of 4.01, and an R^2 of 0.91