101 resultados para Regime indicators


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Community-based cancer organizations provide services to support patients. An anticipated benefit of these services is patient empowerment. However, this outcome has not been evaluated because of the lack of validated health-related empowerment questionnaires in the cancer context. In this validation study, the authors assessed the extent to which 16 indicators used by the Canadian Cancer Society (CCS) and the Cancer Council Victoria, Australia (CCV) to evaluate their services were associated with health-related empowerment.

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Health literacy is a multidimensional concept covering a range of cognitive and social skills necessary for participation in health care. Knowledge of health literacy levels in general populations and how health literacy levels impacts on social health inequity is lacking. The primary aim of this study was to perform a population-based assessment of dimensions of health literacy related to understanding health information and to engaging with healthcare providers. Secondly, the aim was to examine associations between socio-economic characteristics with these dimensions of health literacy.

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Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results.

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In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption.

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This paper investigates empirically the persistence in exchange rate regimes as well as the role of capital account openness and financial sector health (measured by financial development and financial sector fragility) in exchange rate regime determination for a panel of 143 countries covering the post-Bretton Woods period. The results demonstrate that while low- and high-income countries exhibit highly persistent exchange rate regimes, middle-income countries display relatively lower persistence. For middle-income countries, capital account openness and the level of financial development play important roles in exchange rate regime choice. The fragility of the financial sector does not affect the exchange rate regime determination.

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Recent scholarship on international agreement design has almost exclusively focused on the public international law area. The literature on regime design in the area of international private law lacks a solid theoretical foundation. Academic writing on public international law's state-centric approach is only amenable to crude transplantation and poses several puzzles in the international private law context. Resolving these puzzles is important because of the proliferation of transnational commercial agreements in areas that were traditionally the province of domestic law. This paper attempts to provide a starting point to address the theoretical vacuum. Part I argues that functionalist, liberal, and realist theories cannot fully explain transnational commercial law agreement design. Part II puts forth a demandeur-centric approach with the aid of examples that span the spectrum from hard law to soft law. Part III concludes that agreement design in transnational commercial law is premised on demandeur preferences and relative power.

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In order to influence global policy effectively, conservation scientists need to be able to provide robust predictions of the impact of alternative policies on biodiversity and measure progress towards goals using reliable indicators. We present a framework for using biodiversity indicators predictively to inform policy choices at a global level. The approach is illustrated with two case studies in which we project forwards the impacts of feasible policies on trends in biodiversity and in relevant indicators. The policies are based on targets agreed at the Convention on Biological Diversity (CBD) meeting in Nagoya in October 2010. The first case study compares protected area policies for African mammals, assessed using the Red List Index; the second example uses the Living Planet Index to assess the impact of a complete halt, versus a reduction, in bottom trawling. In the protected areas example, we find that the indicator can aid in decision-making because it is able to differentiate between the impacts of the different policies. In the bottom trawling example, the indicator exhibits some counter-intuitive behaviour, due to over-representation of some taxonomic and functional groups in the indicator, and contrasting impacts of the policies on different groups caused by trophic interactions. Our results support the need for further research on how to use predictive models and indicators to credibly track trends and inform policy. To be useful and relevant, scientists must make testable predictions about the impact of global policy on biodiversity to ensure that targets such as those set at Nagoya catalyse effective and measurable change.

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Global biodiversity indicators can be used to measure the status and trends of biodiversity relating to Convention on Biological Diversity (CBD) targets. Whether such indicators can support decision makers by distinguishing among policy options remains poorly evaluated. We tested the ability of two CBD indicators, the Living Planet Index and the Red List Index, to reflect projected changes in mammalian populations in sub-Saharan Africa in response to potential policies related to CBD targets for protected areas (PAs). We compared policy scenarios to expand the PA network, improve management effectiveness of the existing network, and combinations of the two, against business as usual. Both indicators showed that more effective management would provide greater benefits to biodiversity than expanding PAs alone. The indicators were able to communicate outcomes of modeled scenarios in a simple quantitative manner, but behaved differently. This work highlights both the considerable potential of indicators in supporting decisions, and the need to understand how indicators will respond as biodiversity changes.

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Coarse woody debris (CWD) is a common structural component of terrestrial ecosystems, and provides important habitat for biota. Fires modify the distribution of CWD, both spatially and temporally. Changes in fire regimes, such as those arising from prescribed burning and changing climatic conditions, make it critical to understand the response of this resource to fire. We created a conceptual model of the effects of fire on logs and dead trees in topographically diverse forests in which trees often survive severe fire. We then surveyed paired sites, in a damp gully and adjacent drier slope, ~3.5. years after a large wildfire in south-eastern Australia. Sites were stratified by fire severity (unburnt, understorey burnt and severely burnt), and fire history (burnt ≤3. years or ≥20. years prior to the wildfire). Both components of the fire regime influenced CWD availability in gullies. Severe wildfire and fire history ≤3. years reduced the volume of small logs (10-30. cm diameter) in gullies, while severe wildfire increased the number of large dead trees in gullies. CWD on slopes was not affected by fire severity or history at ~3.5. years post-fire. Log volumes on slopes may recover more quickly after wildfire through rapid collapse of branches and trees. Gullies generally supported more logs than slopes, but longer inter-fire intervals in gullies may allow fuel loads to accumulate and lead to comparatively larger fire impacts. Given that fire severity and fire interval are predicted to change in many fire-prone ecosystems in coming decades, this study highlights the importance of understanding the interacting effects of multiple components of the fire regime with landscape structure. In particular, variation in fire interval and fire severity in relation to topographic position will influence the pattern of accumulation of coarse woody debris across the landscape, and therefore the structure and quality of habitats for biota.

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Wildfires have major impacts on ecosystems globally. Fire regimes (including fire frequency, intensity, season and type of fire) influence the status of species by altering habitat suitability at the site scale, and by creating heterogeneity at the landscape scale. The relative effects of site and landscape-scale fire attributes on animal species are rarely examined together. Such knowledge is important, given that fire regimes are sensitive to changing land management practices; and that fires are predicted to become larger and more frequent in some regions as a result of climate change. Here, we tested the relative influence of elements of the fire regime (fire severity, fire history) at the site-scale, and the landscape context (extent of surrounding unburnt forest, fire heterogeneity) on the occurrence of native terrestrial mammals after severe wildfire in south-eastern Australia. We conducted surveys by using automatically triggered, infrared cameras at 80 sites in fire-prone eucalypt forests, 2-3. years post-wildfire. Thirteen native mammal species were recorded, eight of which were detected with sufficient frequency for analysis. Most species were widespread (35-90% of sites) and recorded in all fire severity classes. Fire effects at the site-level were more influential than landscape context effects arising from heterogeneity in the fire regime (e.g. extent of surrounding unburnt forest). Fire severity was the most influential of the fire-regime elements investigated, but it affected different species in different ways. This study highlights three main points relevant to conservation of terrestrial mammals after wildfire. First, spatial variation in fire severity associated with wildfire (ranging from unburned to severely burned stands) is an important contributor to the post-fire status of species. Second, post-fire environmental conditions are significant: here, rapid regeneration of vegetation following drought-breaking rains greatly influenced the suitability of post-fire habitats. Third, it is valuable to consider the effects of the fire regime at multiple scales, including both the site (forest stand) and its landscape context. Insights from short-term surveys, such as this, will be enhanced by complementary longitudinal studies, especially where they encompass environmental variation through the post-fire succession.