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The 2020 update of the Canadian Stroke Best Practice Recommendations (CSBPR) for the Secondary Prevention of Stroke includes current evidence-based recommendations and expert opinions intended for use by clinicians across a broad range of settings. They provide guidance for the prevention of ischemic stroke recurrence through the identification and management of modifiable vascular risk factors. Recommendations address triage, diagnostic testing, lifestyle behaviors, vaping, hypertension, hyperlipidemia, diabetes, atrial fibrillation, other cardiac conditions, antiplatelet and anticoagulant therapies, and carotid and vertebral artery disease. This update of the previous 2017 guideline contains several new or revised recommendations. Recommendations regarding triage and initial assessment of acute transient ischemic attack (TIA) and minor stroke have been simplified, and selected aspects of the etiological stroke workup are revised. Updated treatment recommendations based on new evidence have been made for dual antiplatelet therapy for TIA and minor stroke; anticoagulant therapy for atrial fibrillation; embolic strokes of undetermined source; low-density lipoprotein lowering; hypertriglyceridemia; diabetes treatment; and patent foramen ovale management. A new section has been added to provide practical guidance regarding temporary interruption of antithrombotic therapy for surgical procedures. Cancer-associated ischemic stroke is addressed. A section on virtual care delivery of secondary stroke prevention services in included to highlight a shifting paradigm of care delivery made more urgent by the global pandemic. In addition, where appropriate, sex differences as they pertain to treatments have been addressed. The CSBPR include supporting materials such as implementation resources to facilitate the adoption of evidence into practice and performance measures to enable monitoring of uptake and effectiveness of recommendations.
To systematically review and synthesise qualitative evidence about determinants of self-management in adults with SMI. The goal is to use findings from this review to inform the design of effective self-management strategies for people with SMI and LTCs.
People living with serious mental illness (SMI) have a reduced life expectancy by around 15–20 years, mainly due to the high prevalence of long-term physical conditions such as diabetes and heart disease. People with SMI face many challenges when trying to manage their physical health. Little is known about the determinants of self-management – managing the emotional and practical issues – of long-term conditions (LTCs) for people with SMI.
Six databases, including CINAHL and MEDLINE, were searched to identify qualitative studies that explored people's perceptions about determinants of self-management in adults with SMI (with or without comorbid LTCs). Self-management was defined according to the American Association of Diabetes Educator's self-care behaviours (AADE7). Determinants were defined according to the Capabilities, Opportunity, Motivations and Behaviours (COM-B) framework. Eligible studies were purposively sampled for synthesis according to the richness of the data (assessed using Ames et al (2017)'s data richness scale), and thematically synthesised.
Twenty-six articles were included in the synthesis. Seven studies focused on self-management of LTCs, with the remaining articles exploring self-management of SMI. Six analytic themes and 28 sub-themes were identified from the synthesis. The themes included: the additional burden of SMI; living with comorbidities; beliefs and attitudes about self-management; support from others for self-management; social and environmental factors; routine, structure and planning. Capabilities for self-management were linked to people's perceptions about the support they received for their SMI and LTC from healthcare professionals, family and friends. Opportunities for self-management were more commonly expressed in the context of social and environmental factors. Motivation for self-management was influenced by beliefs and attitudes, whilst being closely related to the burden of SMI.
The themes identified from the synthesis suggest that capabilities, opportunities and motivations for self-management can be negatively influenced by the experience of SMI, whilst social and professional support, improved access to resources, and increased involvement in care, could promote self-management. Support programmes for people with SMI and LTCs need to account for these experiences and adapt to meet the unique needs of this population.
Numerous developmental scholars have been influenced by the research, policies, and thinking of the late Edward Zigler, who was instrumental in founding Head Start and Early Head Start. In line with the research and advocacy work of Zigler, we discuss two models that support the development of the whole child. We begin by reviewing how adverse and protective experiences “get under the skin” and affect developmental trajectories and risk and resilience processes. We then present research and examples of how experiences affect the whole child, the heart and the head (social, emotional, cognitive, and physical development), and consider development within context and across domains. We discuss examples of interventions that strengthen nurturing relationships as the mechanism of change. We offer a public health perspective on promoting optimal development through nurturing relationships and access to resources during early childhood. We end with a discussion of the myth that our current society is child-focused and argue for radical, essential change to make promoting optimal development for all children the cornerstone of our society.
Edward Zigler's groundbreaking research on child development resulted in the historic Head Start program. It is useful to examine the theoretical implications of his work by applying a human development theoretical perspective. Phenomenological variant of ecological systems theory (PVEST) is a strengths-based theoretical framework that engages the variability of resource access and coping strategies that promote positive identity development for diverse children. While skill acquisition is a key focus of human capital theory's engagement of early childhood needs, this article highlights the on-going status of human vulnerability that undergirds identity development over the life course. The authors note that “inequality presence denial” combines with high-risk contexts, framed by geography and psychohistoric moments (e.g., The Great Recession, COVID-19), to alter diverse children's developmental pathways. The acknowledgement of “morbid risk” motivates the urgency for research that builds upon Zigler's innovations and privileges human development imperatives. The case study explores these concepts by examining the challenges and assets available to mothers in a low-income community. The article's closing notes developments in the field of economics that ameliorate human capital theory's conceptual limitations, underscoring human development's theoretical strength in motivating research and policies that are maximally responsive to children's positive identity development.
To what extent does bias correction and downscaling increase the value of GCM outputs for regional-scale applications? This chapter provides an overview of the concept of added value for downscaling studies and discusses the methods and metrics used for evaluating the value that bias correction and downscaling, using an RCM and/or an ESDM, adds to climate projections for impact assessments
Previous chapters have laid out the process of creating high-resolution climate projections at spatial and time scales appropriate for assessing the impacts of climate change at the local to regional scale, and described available assessments, global climate model archives, and both RCM- and ESDM-based high-resolution projections. This chapter builds on this information to discuss the process by which high-resolution climate projections can be selected, applied, and interpreted to quantify future impacts for a given location, system, or assessment.
Global models of the Earth’s climate system are the primary tool scientists use to understand the Earth’s climate system. They yield critical insights into the components of the earth’s climate system, including the atmosphere, land, oceans, and biosphere, the processes at work within and between them, and how natural factors and human activities affect climate at the regional to global scale. This chapter summarizes the evolution of climate modeling and describes current global climate models and how they are being used to study the changing climate.
Although climate change is a global issue, its impacts are experienced primarily at the local to regional scale. This chapter describes important aspects of regional climate and how climate projections can be used to assess climate impacts at the regional to local scale. It summarizes projections and sources of information on changes in continental-scale annual and seasonal temperature and precipitation, climate and weather extremes, and sea-level rise projections.
Dynamical downscaling uses high-resolution regional climate models (RCMs) to bias-correct and downscale global climate model output. This chapter discusses the models and methods used in dynamical downscaling. It provides an overview of the basic physics used in RCMs, and how this is similar to and differs from that used in global models. It also discusses the methods and metrics used to evaluate RCMs, and how projections from RCMs can be used to assess climate impacts at the regional scale
What lies in the future of regional downscaling, and how will future developments advance the information available for impacts and policymaking analyses? Models and methods are constantly under development; what might users expect to become available over the next decades as computing resources reach and extend beyond exascale (over 100 times faster than the current fastest computers), and global climate model resolutions reach regional scales? What uncertainties will remain to be resolved, even with these advances? By its nature, much of this discussion is somewhat philosophical and this chapter does not include real-world case studies. Instead, it highlights specific ways assessments may be affected by future advances and discusses key areas of future development.
Climate change is a broad-reaching, global challenge. It impacts most human and natural systems, from agriculture and ecosystems to energy and health, and exacerbates other preexisting issues, from poverty to political instability. Evaluating these impacts and our vulnerability to them increases awareness of the need for adaptation and resilience. This chapter provides a brief history of impact assessments, focusing on the models, tools, and information that is needed and is available to quantify future impacts across a wide range of systems and scales and to provide valuable input to adaptation and resilience planning
Empirical-statistical downscaling combines observations with global climate model outputs to generate high-resolution spatial and temporal projections. This chapter describes some of the common methods and models used in spatial and temporal disaggregation and bias correction, focusing on aspects of their design and performance that are relevant to their application for quantifying local to regional-scale impacts.
Future projections are uncertain, for multiple reasons. Limits to scientific understanding of natural variability, structural and parametric uncertainty in scientific modeling, climate sensitivity, bias correction and downscaling all play a role. The uncertainty due to human choices that will determine emissions of heat-trapping gases becomes increasingly important over time, to the point where it dominates the uncertainty in many aspects of global and regional change by the end of century. Quantifying how a given system will respond to a changing climate adds yet another layer of uncertainty that can be prohibitively large in systems that are complex and/or not well understood. Understanding the source of these uncertainties and how they can be addressed when applying downscaled climate projections to assess future impacts is essential to quantifying the range of future change and resulting impacts on human and natural systems
Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for policy makers and practitioners interested in climate risk and resilience.