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Cognitive impairment is common in people with mental disorders, leading to transdiagnostic classification based on cognitive characteristics. However, few studies have used this approach for intellectual abilities and functional outcomes.
Aims
The present study aimed to classify people with mental disorders based on intellectual abilities and functional outcomes in a data-driven manner.
Method
Seven hundred and forty-nine patients diagnosed with schizophrenia, bipolar disorder, major depression disorder or autism spectrum disorder and 1030 healthy control subjects were recruited from facilities in various regions of Japan. Two independent k-means cluster analyses were performed. First, intelligence variables (current estimated IQ, premorbid IQ, and IQ discrepancy) were included. Second, number of work hours per week was included instead of premorbid IQ.
Results
Four clusters were identified in the two analyses. These clusters were specifically characterised in terms of IQ discrepancy in the first cluster analysis, whereas the work variable was the most salient feature in the second cluster analysis. Distributions of clinical diagnoses in the two cluster analyses showed that all diagnoses were unevenly represented across the clusters.
Conclusions
Intellectual abilities and work outcomes are effective classifiers in transdiagnostic approaches. The results of our study also suggest the importance of diagnosis-specific strategies to support functional recovery in people with mental disorders.
By
Arnulf Grubler, International Institute for Applied Systems Analysis, Austria and Yale University,
Xuemei Bai, Australian National University,
Thomas Buettner, United Nations Department of Economic and Social Affairs,
Shobhakar Dhakal, Global Carbon Project and National Institute for Environmental Studies,
David J. Fisk, Imperial College London,
Toshiaki Ichinose, National Institute for Environmental Studies,
James E. Keirstead, Imperial College London,
Gerd Sammer, University of Natural Resources and Applied Life Sciences,
David Satterthwaite, International Institute for Environment and Development,
Niels B. Schulz, International Institute for Applied Systems Analysis, Austria and Imperial College,
Nilay Shah, Imperial College London,
Julia Steinberger, The Institute of Social Ecology, Austria and University of Leeds,
Helga Weisz, Potsdam Institute for Climate Impact Research,
Gilbert Ahamer, University of Graz,
Timothy Baynes, Commonwealth Scientific and Industrial Research Organisation,
Daniel Curtis, Oxford University Centre for the Environment,
Michael Doherty, Commonwealth Scientific and Industrial Research Organisation,
Nick Eyre, Oxford University Centre for the Environment,
Junichi Fujino, National Institute for Environmental Studies,
Keisuke Hanaki, University of Tokyo,
Mikiko Kainuma, National Institute for Environmental Studies,
Shinji Kaneko, Hiroshima University,
Manfred Lenzen, University of Sydney,
Jacqui Meyers, Commonwealth Scientific and Industrial Research Organisation,
Hitomi Nakanishi, University of Canberra,
Victoria Novikova, Oxford University Centre for the Environment,
Krishnan S. Rajan, International Institute of Information Technology,
Seongwon Seo, Commonwealth Scientific and Industrial Research Organisation,
Ram M. Shrestha, Asian Institute of Technology,
Priyadarshi R. Shukla, Indian Institute of Management,
Alice Sverdlik, International Institute for Environment and Development,
Jayant Sathaye, Lawrence Berkeley National Laboratory
More than 50% of the global population already lives in urban settlements and urban areas are projected to absorb almost all the global population growth to 2050, amounting to some additional three billion people. Over the next decades the increase in rural population in many developing countries will be overshadowed by population flows to cities. Rural populations globally are expected to peak at a level of 3.5 billion people by around 2020 and decline thereafter, albeit with heterogeneous regional trends. This adds urgency in addressing rural energy access, but our common future will be predominantly urban. Most of urban growth will continue to occur in small-to medium-sized urban centers. Growth in these smaller cities poses serious policy challenges, especially in the developing world. In small cities, data and information to guide policy are largely absent, local resources to tackle development challenges are limited, and governance and institutional capacities are weak, requiring serious efforts in capacity building, novel applications of remote sensing, information, and decision support techniques, and new institutional partnerships. While ‘megacities’ with more than 10 million inhabitants have distinctive challenges, their contribution to global urban growth will remain comparatively small.
Energy-wise, the world is already predominantly urban. This assessment estimates that between 60–80% of final energy use globally is urban, with a central estimate of 75%. Applying national energy (or GHG inventory) reporting formats to the urban scale and to urban administrative boundaries is often referred to as a ‘production’ accounting approach and underlies the above GEA estimate.
Historically, economic development has been strongly correlated with increasing energy use and growth of greenhouse gas (GHG) emissions. Renewable energy (RE) can help decouple that correlation, contributing to sustainable development (SD). In addition, RE offers the opportunity to improve access to modern energy services for the poorest members of society, which is crucial for the achievement of any single of the eight Millennium Development Goals.
Theoretical concepts of SD can provide useful frameworks to assess the interactions between SD and RE. SD addresses concerns about relationships between human society and nature. Traditionally, SD has been framed in the three-pillar model—Economy, Ecology, and Society—allowing a schematic categorization of development goals, with the three pillars being interdependent and mutually reinforcing. Within another conceptual framework, SD can be oriented along a continuum between the two paradigms of weak sustainability and strong sustainability. The two paradigms differ in assumptions about the substitutability of natural and human-made capital. RE can contribute to the development goals of the three-pillar model and can be assessed in terms of both weak and strong SD, since RE utilization is defined as sustaining natural capital as long as its resource use does not reduce the potential for future harvest.
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