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This paper describes a study of residential neighborhoods. Data from a number of sources were used to identify ten independent dimensions which appeared intuitively meaningful as descriptors of residential settings. The independence of the dimensions indicates that stereotyped characterizations of the 'urban/rural' or the 'affluent/nonaffluent' type are unable to capture the multidimensional character of residential neighborhoods. It was hypothesized that the dimensions could describe some of the humane characteristics of residential neighborhoods, and a study of well-being in former mental patients lent some support to this hypothesis. Although this was a pilot study and much work remains to be done, the results are encouraging in that they appear to be applicable to planning for the community care of the mentally ill; to the design of humane environments; and as a methodological link in identifying some of the environmental components of psychological well-being.

This paper investigates the effect of proximity on attitudes towards mental illness. One outcome of the policies referred to as deinstitutionalization is an overall reduction in the social and geographical distance between members of the public and the mentally ill. For deinstitutionalization to be successful, it is important that residents in impacted communities become more accepting, both in a passive and in an active sense. A survey was made of residents in two neighborhoods of the city of Norman, Oklahoma, one of which was adjacent to a large mental health facility. In a causal model framework, the relationship between proximity and public attitudes is investigated; and the results suggest an important and significant relationship. The implications of the study for public education campaigns about mental illness, and for facility location strategies, are discussed.

Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850–1900, using an observationally based historical warming estimate of 0.8°C between 1850–1900 and 1995–2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections. ; ARC Centre of Excellence for Climate Extremes CE170100023 United States Department of Energy (DOE) Aparece en contenido como:U.S. Department of Energy, Office of Science, research in MultiSector Dynamics, Earth and Environmental System Modeling Program NERC/IIASA Collaborative Research Fellowship NE/T009381/1 Integrated Research Program for Advancing Climate Models (TOUGOU), Grant from the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan JPMXD0717935457 U.S. National Aeronautics and Space Administration Climate Indicators and Data Products for Future National Climate Assessments program NX16AG34G European Union's Horizon 2020 research and innovation programme 820829 ; Versión publicada - versión final del editor