Safe food, fair food for Cambodia

United States Agency for International Developmen

eJournal interface can influence usage statistics: implications for libraries, publishers, and Project COUNTER

The design of a publisher's electronic interface can have a measurable effect on electronic journal usage statistics. A study of journal usage from six COUNTER-compliant publishers at thirty-two research institutions in the United States, the United Kingdom and Sweden indicates that the ratio of PDF to HTML views is not consistent across publisher interfaces, even after controlling for differences in publisher content. The number of fulltext downloads may be artificially inflated when publishers require users to view HTML versions before accessing PDF versions or when linking mechanisms, such as CrossRef, direct users to the full text, rather than the abstract, of each article. These results suggest that usage reports from COUNTER-compliant publishers are not directly comparable in their current form. One solution may be to modify publisher numbers with adjustment factors deemed to be representative of the benefit or disadvantage due to its interface. Standardization of some interface and linking protocols may obviate these differences and allow for more accurate cross-publisher comparisons.Comment: 22 pages, 5 figures. JASIST (in press, 2006

A practitioner’s handbook : optimizing conservation and improving mitigation through the use of progressive approaches

The information contained in this report was prepared as part of NCHRP Project 25-25, Task 67, National Cooperative Highway Research Program, Transportation Research Board. SPECIAL NOTE: This report IS NOT an official publication of the National Cooperative Highway Research Program, Transportation Research Board, National Research Council, or The National Academies.When proposed for permitting under the terms of the Clean Water Act (CWA) §404 and Endangered Species Act (ESA) §7 and §10 programs, many transportation, infrastructure, and development projects would cause impacts to wetlands, streams, and the habitat of sensitive species. In these cases, state and regional transportation agencies work with Federal and state regulatory agencies to avoid and minimize adverse impacts to aquatic resources and habitat. After impacts to aquatic resources and habitat are avoided and minimized as much as possible, transportation agencies are commonly required to compensate for unavoidable impacts to these resources. Compensation, or compensatory mitigation, can be an important method of maintaining healthy, economically valuable ecosystems. In this handbook we provide an overarching view of the ecosystem and economic benefits and cost savings associated with progressive approaches to the Clean Water Act or Endangered Species Act compensatory mitigation and com-pare these benefits and savings to traditional mitigation approaches. We then highlight several empirical examples of transferable tools, models, and frame-works used for innovative compensatory mitigation in use throughout the United States. We highlight innovative tools, methods, and frameworks that focus on landscape or watershed analysis of ecosystem functions only, as well as progressive approaches that include the valuation of ecosystem services provided by compensatory mitigation. Finally, we lay out tangible steps for transportation agencies, policy-makers, and the research community to facilitate and implement progressive mitigation programs. More detailed information about some important technical terms are provided in call-out boxes throughout the handbook, and a glossary of terms is provided in Section 6

What do you do for a living? Toward a more succinct definition of health services research

This commentary discusses the need for, and the advantages of, a more concise, revised definition of the field of health services research. It argues for a definition that includes not only the topics on which health services research focuses but also the goals of health services research. A number of condensed definitions are provided for consideration

医療AIソフトウェアシステムの開発と実装を推進するためのタイプ分類の提案

In recent years, artificial intelligence (AI) systems including machine learning, are expected to be used to reduce the burden on doctors and healthcare workers. However, there are many challenges in clinical implementation. In order to facilitate discussions on medical AI software systems among healthcare professionals, technology developers, policy makers, and public/patients, this paper proposed a type classification for medical AI systems (MA Type). In addition to technical requirements, we have developed a classification system that includes the perspectives of user interface, institutional design, and the role and impact on health professionals and patients/users. In developing and implementing medical AI systems, we hope that MA types will be used to share awareness among healthcare professionals and technology developers. The following three recommendations are made regarding the use of MA types and their future development

AIサービスのリスク低減を検討するリスクチェーンモデルの提案

With the increasing use of artificial intelligence (AI) services and products in recent years, issues related to their trustworthiness have emerged and AI service providers need to be prepared for various risks. In this policy recommendation, we propose a risk chain model (RCModel) that supports AI service providers in proper risk assessment and control. We hope that RCModel will contribute to the realization of trustworthy AI services.Overview of RCModel 1)Organization and structure of risk componentsThere are a number of potential risk factors involved in provision of AI services. In RCModel, these factors are segregated into (1) technical components of the AI system, (2) components related to the code of conduct (including communication with users) of the service provider, and (3) components related to the user’s understanding, behavior, and usage environment.2)Identification of risk scenarios and risk-contributing factorsRCModel helps identify risk scenarios related to AI services, such as unfair decisions and uncontrollable accidents. It then identifies risk factors for priority risk scenarios.3)Visualization of risk chains and planning risk controlBecause it is difficult to reduce risk sufficiently on a factor basis, AI service providers can consider stepwise risk reduction by visualizing the relationships (risk chain) among the risk factors related to risk scenarios. This allows consideration of where a risk exists and its effective and efficient control.Policy Recommendations for Future Development and Implementation of AI Services Using RCModelPolicy Recommendation 1: Enhance understanding of risk scenarios and factorsService providers need to properly understand the risk factors associated with their AI services. They should also pay attention to social incidents involving the use of AI technologies and recognize important risk scenarios.Policy Recommendation 2：Promotion of appropriate risk controls using RCModelAI service providers should formulate their risk control measures by analyzing RCModel’s risk chain. It is neither necessary nor always possible to reduce all the risks identified; therefore, appropriate controls should be established within an enterprise based on factors such as magnitude of risks posed, technical difficulty, cost-effectiveness, and continuity.Policy Recommendation 3：Promoting and updating dialogue among stakeholdersRCModel should be used to facilitate dialogue among AI service providers, AI developers, and users. In addition, a system should be established to clarify risk tolerance, create risk scenarios, structure risk factors, examine risk control models, and create a common understanding on the scope of each stakeholder’s responsibility

Development of Geriatric Competencies for Emergency Medicine Residents Using an Expert Consensus Process

The emergency department (ED) visit rate for older patients exceeds that of all age groups other than infants. The aging population will increase elder ED patient utilization to 35% to 60% of all visits. Older patients can have complex clinical presentations and be resource-intensive. Evidence indicates that emergency physicians fail to provide consistent high-quality care for elder ED patients, resulting in poor clinical outcomes.The objective was to develop a consensus document, “Geriatric Competencies for Emergency Medicine Residents,” by identified experts. This is a minimum set of behaviorally based performance standards that all residents should be able to demonstrate by completion of their residency training.This consensus-based process utilized an inductive, qualitative, multiphase method to determine the minimum geriatric competencies needed by emergency medicine (EM) residents. Assessments of face validity and reliability were used throughout the project.In Phase I, participants ( n =  363) identified 12 domains and 300 potential competencies. In Phase II, an expert panel ( n =  24) clustered the Phase I responses, resulting in eight domains and 72 competencies. In Phase III, the expert panel reduced the competencies to 26. In Phase IV, analysis of face validity and reliability yielded a 100% consensus for eight domains and 26 competencies. The domains identified were atypical presentation of disease; trauma, including falls; cognitive and behavioral disorders; emergent intervention modifications; medication management; transitions of care; pain management and palliative care; and effect of comorbid conditions.The Geriatric Competencies for EM Residents is a consensus document that can form the basis for EM residency curricula and assessment to meet the demands of our aging population.ACADEMIC EMERGENCY MEDICINE 2010; 17:316–324 © 2010 by the Society for Academic Emergency MedicinePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79220/1/j.1553-2712.2010.00684.x.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/79220/2/ACEM_684_sm_DataSupplementS1.pd