York University
  • Toronto, Canada
Recent publications
This chapter asks whether thinking about space goes far enough in comprehending the ocean, arguing that scholars must engage more with materiality and temporality to discover new forms of ocean governance. Noting the important scholarly work that has moved beyond a planar, or areal, conceptualisation of the ocean, the chapter draws on recent scholarship to critically assess volumetric theorisations that do not sufficiently account for the fluidity of matter within the ocean. It contends that scholars must think through the complex materiality of the ocean by conceptualising the highly mobile stuff within it as being an inherent part of ocean space itself. It then argues that to think about the materiality of the ocean and how it moves and flows, one must engage with temporality, helping one to move beyond both two-dimensional thinking and three-dimensional volumetrics towards an understanding of the ocean as processual and something that happens in time. Surveying recent literature on relational approaches to space, the chapter argues that the future of ocean governance must be multiple, complex, and messy, rather than overdetermined by a political logic of territory that reifies state borders as a byproduct of its legal mechanisms.
Children who engage in aggressive behaviors are at heightened risk of being involved in the justice system, committing serious offenses, and becoming chronic offenders. The Stop Now And Plan (SNAP) program was designed as an early intervention to address several mechanisms underlying the development of conduct problems, including emotion regulation, prosocial behaviors, and parent–child relationships. The purpose of this study was to systematically review and synthesize current research on the SNAP program and conduct a meta-analysis. Following PRISMA guidelines, PubMed and PyscINFO were searched, and the developers of SNAP were contacted to ensure no articles were missed. Twenty-two peer-reviewed articles were ultimately included following a two-stage screening process. The meta-analysis revealed a moderate effect size change (SMD = − 0.54, 95% CI [- .42, - .65], p < .001) in externalizing problems from pre- to post-SNAP Group. The narrative review found evidence for decreases in symptoms (e.g., conduct problems, aggression, delinquency, internalizing problems) across SNAP programming. There was also preliminary evidence for changes in proposed mechanisms across the groups (e.g., emotion regulation, parent behaviors, child-parent relationship). Two randomized controlled trials (RCT) supported the efficacy of the SNAP Boys Group over another active treatment. One waitlist control found similar results for the SNAP Girls Group. There is growing evidence for SNAP, attributable to the effective clinical research partnerships established by the developers. More rigorous methods and RCTs will help solidify SNAP as a top evidence-based intervention.
Background Understanding the key drivers of SARS-CoV-2 transmission is essential for shaping effective public health strategies. However, transmission risk is subject to substantial heterogeneity related to disease severity, age, sex, comorbidities, and vaccination status in different population settings and regions. We aimed to quantify the impact of these factors on secondary attack rates (SARs) of SARS-CoV-2 across diverse population settings and regions, and identify key determinants of transmission to inform targeted interventions for improving global pandemic response. Methods To retrieve relevant literature covering the duration of the COVID-19 pandemic, we searched Ovid MEDLINE, Ovid Embase, Web of Science, and the Cochrane COVID-19 Study Register between January 1, 2020 and January 18, 2024 to identify studies estimating SARs of SARS-CoV-2, defined as the proportion of close contacts infected. We pooled SAR estimates using a random-effects model with the Freeman-Tukey double arcsine transformation and derived Clopper-Pearson 95% confidence intervals (CIs). Risk of bias was assessed using a modified Newcastle–Ottawa scale. This study was registered with PROSPERO, CRD42024503782. Results A total of 159 eligible studies, involving over 19 million close contacts and 6.8 million cases from 41 countries across five continents, were included in the analysis. SARs increased with disease severity in index cases, ranging from 0.10 (95% CI: 0.06–0.14; I² = 99.65%) in asymptomatic infection to 0.15 (95% CI: 0.09–0.21; I² = 92.49%) in those with severe or critical conditions. SARs by age were lowest at 0.20 (95% CI: 0.16–0.23; I² = 99.44%) for close contacts under 18 years and highest at 0.29 (95% CI: 0.24–0.34; I² = 99.65%) for index cases aged 65 years or older. Among both index cases and close contacts, pooled SAR estimates were highest for Omicron and lowest for Delta, and declined with increasing vaccine doses. Regionally, North America had the highest SAR at 0.27 (95% CI: 0.24–0.30; I² = 99.31%), significantly surpassing SARs in Europe (0.19; 95% CI: 0.15–0.25; I² = 99.99%), Southeast Asia (0.18; 95% CI: 0.13–0.24; I² = 99.24%), and the Western Pacific (0.11; 95% CI: 0.08–0.15; I² = 99.95%). Among close contacts with comorbidities, chronic lung disease and hypertension were associated with the highest SARs. No significant association was found between SARs and the sex of either index cases or close contacts. Conclusions Secondary attack rates varied substantially by demographic and regional characteristics of the studied populations. Our findings demonstrate the role of booster vaccinations in curbing transmission, underscoring the importance of maintaining population immunity as variants of SARS-CoV-2 continue to emerge. Effective pandemic responses should prioritise tailored interventions that consider population demographics and social dynamics across different regions.
This paper presents a low-cost, portable sensing platform for rapid DNA mass measurement, addressing a critical need in life science research. The platform features a novel interdigital open-gate junction field-effect transistor (ID-OGJFET) with a large sensing area that converts negatively charged DNA mass into an electrical current. The system enables DNA mass detection in under ten seconds with a resolution of less than 1 µA, demonstrating sensitivity across a range from 0.48 ng to 29.5 ng, achieving a Limit of Detection as low as 1.18–1.25 ng/µL. A custom-designed electronic reader and fluidic sample holder facilitate efficient operation. Simulation studies using molecular dynamics and finite element methods provide further insights into the sensor’s DNA detection mechanism. This highly sensitive system is significantly more cost-effective than commercially available semiconductor characterization alternatives. The device’s high performance and affordability make it a valuable tool for molecular biology applications, and it holds potential for advancing FET-based sensing instrumentation and measurement research. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-89082-1.
Regular dental visits are crucial for maintaining optimal oral health, yet adequate access to oral healthcare services remains a significant challenge for refugee populations, including resettled Syrian refugees in Ontario. This study aimed to assess the factors associated with visiting the dentist only for emergency care among resettled Syrian refugee parents in Ontario. A cross-sectional study of 540 Syrian refugee parents, who had resided in Ontario for an average of four years and had at least one child less than 18 years of age, was conducted from March 2021 to March 2022. Information about visiting the dentist only for emergency reasons was gathered through the question, “How often do you usually visit the dentist?” Respondents who indicated that they visit the dentist only for emergency care were categorized as “yes.” Multivariable logistic regression was performed to examine the relationship between each of the sociodemographic-, migration-, and health-related factors with the primary outcome of visiting the dentist only for emergency. 73% of respondents reported visiting the dentist only for emergencies. Factors associated with emergency dental visits included dental insurance, number of children, and self-rated oral health. Individuals without dental insurance, higher number of children, and poorer self-rated oral health were more likely to seek emergency dental care. These findings highlight the barriers to regular dental visits among Syrian refugees in Ontario and underscore the need for more inclusive and accessible dental care services for such vulnerable population to enhance their oral health outcomes.
Climate change and rapid urbanization have intensified the frequency and severity of flooding, resulting in substantial damage to communities and infrastructure. Existing research on flood risk addresses a wide range of dimensions, ranging from physical to managerial aspects, which adds complexity to the assessment process. This paper introduces the Integrated Risk Linkages (IRL) Framework to provide a systematic approach to flood risk assessment. The IRL Framework defines risk as the intersection of hazard and vulnerability, where vulnerability is shaped by exposure and susceptibility. Resilience, including coping and adaptive capacities, serves as a counterbalance to vulnerability, offering pathways to mitigate flood impacts. Guided by the IRL framework, this study conducts a comprehensive review of the literature to identify and organize a detailed set of 99 criteria and sub-criteria into three overarching hierarchical structures: hazard, susceptibility, and resilience. Furthermore, the paper evaluates existing flood risk assessment methods, emphasizing their characteristics and practical applicability. The IRL framework presented in this study offers essential insights for navigating the complexities of flood risk management, serving as a valuable reference for researchers, policymakers, and practitioners. Its flexibility empowers users to adapt the framework by utilizing specific components or its entire hierarchical structure, depending on data availability and research objectives, thereby enhancing its applicability across diverse contexts.
To investigate the effect of a 6-week tai chi (TC) training program on physical performance in adolescents with Down syndrome and its influence on the psychological well-being of their parents, in a randomized controlled design, 25 male adolescents with Down syndrome (age 14.4 ± 1.30 years) were randomly assigned to a control group ( n = 10) or a training group ( n = 15). Before and after the training period, lower limb explosive strength, upper limb strength, flexibility, and balance were assessed in all participants, as well as their parents’ psychological well-being. Using 2 × 2 repeated-measures analysis of variance, significant Group × Time interactions ( p < .05; ) were found for physical measures and parents’ depression, anxiety, and stress symptoms. A 6-week TC program significantly improved lower limb explosive strength ( p < .001; d = 1.21), upper limb strength ( p < .001; d = 1.49), flexibility ( p < .001; d = 1.11), and static balance ( p < .001; d = 1.99) and reduced depression ( p < .001; d = 1.89), anxiety ( p < .001; d = 1.74), and stress scores ( p < .001; d = 1.88) in parents in the training group compared with those in the control group. TC programs improve physical measures in adolescents with Down syndrome and psychological well-being of their parents. Establishing TC programs in sport associations could positively impact this population’s physical performance.
Objective To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions. Design Grounded theory qualitative interview study. Setting Primarily single-site at a major urban academic paediatric hospital, with external sampling. Participants Sixteen participants representing physicians (n=10), nursing (n=3), respiratory therapists (n=2) and an ML specialist (n=1) with experience working in acute care environments were identified through purposive sampling. Individuals were recruited to represent a spectrum of ML knowledge (three expert, four knowledgeable and nine non-expert) and years of experience (median=12.9 years postgraduation). Recruitment proceeded through snowball sampling, with individuals approached to represent a diversity of fields, levels of experience and attitudes towards artificial intelligence (AI)/ML. A member check step and consultation with patients was undertaken to vet the framework, which resulted in some minor revisions to the wording and framing. Interventions A semi-structured virtual interview simulating an intensive care unit handover for a hypothetical patient case using a simulated ML model and seven visualisations using known methods addressing interpretability of models in healthcare. Participants were asked to make an initial care plan for the patient, then were presented with a model prediction followed by the seven visualisations to explore their judgement and potential influence and understanding of the visualisations. Two visualisations contained contradicting information to probe participants’ resolution process for the contrasting information. The ethical justifiability and clinical reasoning process were explored. Main outcome A comprehensive framework was developed that is grounded in established medicolegal and ethical standards and accounts for the incorporation of inference from ML models. Results We found that for making good decisions, participants reflected across six main categories: evidence, facts and medical knowledge relevant to the patient’s condition; how that knowledge may be applied to this particular patient; patient-level, family-specific and local factors; facts about the model, its development and testing; the patient-level knowledge sufficiently represented by the model; the model’s incorporation of relevant contextual factors. This judgement was centred on and anchored most heavily on the overall balance of benefits and risks to the patient, framed by the goals of care. We found evidence of automation bias, with many participants assuming that if the model’s explanation conflicted with their prior knowledge that their judgement was incorrect; others concluded the exact opposite, drawing from their medical knowledge base to reject the incorrect information provided in the explanation. Regarding knowledge about the model, we found that participants most consistently wanted to know about the model’s historical performance in the cohort of patients in their local unit where the hypothetical patient was situated. Conclusion Good decisions using AI tools require reflection across multiple domains. We provide an actionable framework and question guide to support clinical decision-making with AI.
Fog constitutes a thick, opaque blanket of air hugging the Earth’s surface, laden with small water droplets or ice crystals. Fog disrupts transportation, poses security threats, disorients human perception and impacts communications and ecosystems. Collusion of atmospheric, terrestrial and hydrologic processes produces fog droplets that pullulate over hygroscopic aerosols that act as condensation nuclei. Marine fog is particularly complex, since underlying dynamic, thermodynamic and (bio)physicochemical processes span fifteen decades of spatial scales, from megameter-sized synoptic weather systems to nanometer-scale bioaerosols. This paper overviews the first international field campaign (Fatima-GB) of the project dubbed Fatima (Fog and turbulence interactions in the marine atmosphere) conducted during 01-31 July, 2022 in the Grand Banks region of North Atlantic. Therein, weather systems and commingling cold and warm oceanic waters provide entrée for fog genesis. Measurement platforms included an islet southwest of Nova Scotia (Sable Island), a research vessel (Atlantic Condor), an offshore Oil Platform and autonomous surface vehicles. The instrument array comprised of extant remote and in-situ sensors augmented by novel sensing systems prototyped and deployed in marine fog to penetrate the smallest scales of turbulence, examine aerosols, and quantify radiation budget. The comprehensive data set so gathered, together with satellite and reanalysis products, mesoscale-model and large-eddy simulations demonstrated that the long-held hypotheses of marine fog formation by warm air advection over colder water and in areas of enhanced (shelf) turbulence need to be revisited. The study also elicited new phenomena, for example, the Fog Shadow (clearings of fog downstream of islands).
Parents of autistic children are at a higher risk for mental health problems, including anxiety, depression, and stress. Cognitive behavior therapy (CBT) that targets children's emotion regulation may have an indirect influence on parent outcomes, especially if they play a supporting role in their child's intervention. However, most CBT interventions have been carried out in highly controlled research settings and there are a few studies that examined parental outcomes after participating in autistic child‐focused CBT within a community setting. The current study examined parent outcomes (i.e., mental health problems, mindful parenting, and parenting practices) following a community‐based CBT program with concurrent parent involvement for autistic children, as well as associations between changes in parent and child outcomes (i.e., autism symptoms and emotion dysregulation). Participants included 77 parent–child dyads across seven community organizations in Ontario, Canada. Parents reported improved mindful parenting and positive parenting practices post‐intervention, and no significant changes in their mental health. Multiple mediation analyses revealed that positive changes in parent outcomes (i.e., mindful parenting and parenting practices) were associated with positive changes in child emotion regulation. These positive changes in parenting practices mediated the relationship between mindful parenting and child emotion regulation. Results suggest that participating in community‐based CBT is mutually beneficial for autistic children and their parents, particularly in improving parenting behaviors.
While standard methods for chlorine taste and odor (T&O) detection and rejection thresholds exist, little rigorous research has been conducted on T&O thresholds in humanitarian settings. To fill this gap, we estimated chlorine T&O detection and rejection thresholds using the Forced-Choice Triangle Test (FCT) and Flavor Rating Assessment (FRA) standard methods in a Ugandan refugee settlement. We conducted these tests with 410 male and female participants, aged 5–72 years, using piped and trucked surface water and bottled water. We also conducted 30 focus group discussions and 37 surveys with data collectors. Median chlorine detection thresholds were 0.56, 1.40, and 1.67 mg/L, for piped, trucked, and bottled water, respectively. Rejection was calculated using ratings (as per the method) and five different previously-used thresholds, and was 1.6, 2.0, and 1.6 mg/L (ratings) and 2.19, 1.85, and 1.67 mg/L (using the FCT threshold method with FRA data) for piped, trucked, and bottled water, respectively. Detection and rejection thresholds were significantly associated with water quality (including turbidity, pH, electrical conductivity, and temperature), participant age and education. We observed high intra- and inter-individual variability, which decreased with participant experience. We found the method used to calculate rejection thresholds influenced results, highlighting the need for a standard method to analyze FRA data. Data collectors and participants recommended shortening protocols and evaluating fewer concentrations, and highlighted difficulties in creating accurate FRC concentrations for testing. This study provides insights on using standard methods to assess T&O thresholds in untrained populations, and results are being used to develop rapid field T&O protocols for humanitarian settings.
Large language models (LLMs) have revolutionized natural language interfaces for databases, particularly in text-to-SQL conversion. However, current approaches often generate unreliable outputs when faced with ambiguity or insufficient context. We present Reliable Text-to-SQL (RTS), a novel framework that enhances query generation reliability by incorporating abstention and human-in-the-loop mechanisms. RTS focuses on the critical schema linking phase, which aims to identify the key database elements needed for generating SQL queries. It autonomously detects potential errors during the answer generation process and responds by either abstaining or engaging in user interaction. A vital component of RTS is the Branching Point Prediction (BPP) which utilizes statistical conformal techniques on the hidden layers of the LLM model for schema linking, providing probabilistic guarantees on schema linking accuracy. We validate our approach through comprehensive experiments on the BIRD benchmark, demonstrating significant improvements in robustness and reliability. Our findings highlight the potential of combining transparent-box LLMs with human-in-the-loop processes to create more robust natural language interfaces for databases. For the BIRD benchmark, our approach achieves near-perfect schema linking accuracy, autonomously involving a human when needed. Combined with query generation, we demonstrate that near-perfect schema linking and a small query generation model can almost match SOTA accuracy achieved with a model orders of magnitude larger than the one we use.
One of the most important means by which we can share our enthusiasm for structural science is our mentorship of trainees. Our trainees at all levels gain more than just technical skills from the time we spend with them; they develop their own appreciation and excitement for structural science that they then can spread through their connections and contacts. We play an important role, through our mentorship, in encouraging that excitement, fostering inquiry, and passing on that excitement to others. We often recount where our enthusiasm began, with one or more professors, mentors and/or colleagues whose excitement was infectious and helped us along our own professional journey and development of our own mentorship philosophies. In the current article, I outline how several mentors, including Professors Michael James, Louis Delbaere, Wilson Quail, and others, instilled that excitement for structural science in me and provided examples from which I have developed my perspective on mentorship and how we can pay it forward, supporting and instilling excitement in our trainees.
This chapter delves into the ethical, social, and practical dimensions of integrating AI and systems thinking in complex decision-making contexts. It explores key ethical issues such as bias, transparency, and accountability in AI systems, emphasizing the need to balance AI capabilities with human ethical standards. The chapter also examines the social impacts of AI, including societal transformations and the imperative to address inequalities. Additionally, it addresses technical, cultural, and organizational challenges associated with integrating AI and systems thinking, offering strategies to overcome these barriers. The goal is to provide a comprehensive understanding of these considerations to guide responsible AI adoption.
This chapter explores the intersection of artificial intelligence (AI) and systems thinking, focusing on their combined application for complex decision-making. It begins with an overview of AI technologies such as machine learning, deep learning, and natural language processing and their role in analyzing complex systems. Systems thinking tools, including causal loop diagrams and stock-and-flow models, are discussed for their effectiveness in understanding system dynamics. The chapter further examines methodologies for integrating AI with systems thinking tools and presents case studies demonstrating their practical applications. This integration offers a comprehensive approach to addressing and managing complexity in various domains.
This chapter explores the future directions and innovations in AI and systems thinking, focusing on emerging technologies and their potential to enhance decision-making. It discusses advancements such as quantum computing and neural-symbolic integration and highlights future research trends in AI and complex systems. The chapter proposes a roadmap for future research, identifying key knowledge gaps and suggesting interdisciplinary research agendas. It introduces a unique model for integrating AI and systems thinking in decision-making, aiming to address modern complexities. The chapter concludes with reflections on the transformative potential of these advancements and their implications for various domains.
This chapter introduces the concept of complexity in modern decision-making, focusing on its increasing relevance in today’s interconnected and dynamic environments. Complexity is defined through its core attributes, including unpredictability, interdependencies, and emergent behaviors. The chapter explores how artificial intelligence (AI) and systems thinking can be synergistically employed to address these challenges, providing decision-makers with tools to navigate and mitigate complex scenarios effectively. The purpose and scope of the book are also outlined, emphasizing the integration of AI and systems thinking as a novel approach to smarter decision-making in complex contexts.
This chapter synthesizes the key insights from the integration of AI and systems thinking in decision-making. It recaps the transformative impact of combining these approaches, emphasizing their application across various domains including business, healthcare, and environmental management. The chapter discusses the practical implications for professionals and highlights the evolving landscape of decision-making. Future directions are explored, with a focus on emerging technologies and the development of interdisciplinary research agendas. The concluding remarks reflect on the potential for AI and systems thinking to shape smarter decisions and address complex challenges in the future.
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