Factors influencing Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction

The purpose of this study was to identify, through in-depth interview, factors that influenced 27 Hong Kong Chinese patients' decision-making in seeking early treatment for acute myocardial infarction (AMI). The median delay time from the onset of symptoms to arrival at the hospital was 15.6 hours for men and 53.7 hours for women. Three major categories emerged from the data: (a) becoming aware of the threat, (b) maintaining a sense of normality, and (c) struggling to mobilize resources. A variety of decisions were made by patients from the onset of chest Pain to seeking help. These decisions were heavily influenced by healthcare factors (access to emergency medical service (EMS) and treatment), personal factors (cognitive interpretations of symptoms), sociocultural factors (family situation, cultural beliefs, and practices), and coping strategies. (c) 2006 Wiley Periodicals, Inc.

Collaborative decision-making processes for maintaining biodiversity: Two Australian case studies

There have been many models developed by scientists to assist decision-makers in making socio-economic and environmental decisions. It is now recognised that there is a shift in the dominant paradigm to making decisions with stakeholders, rather than making decisions for stakeholders. Our paper investigates two case studies where group model building has been undertaken for maintaining biodiversity in Australia. The first case study focuses on preservation and management of green spaces and biodiversity in metropolitan Melbourne under the umbrella of the Melbourne 2030 planning strategy. A geographical information system is used to collate a number of spatial datasets encompassing a range of cultural and natural assets data layers including: existing open spaces, waterways, threatened fauna and flora, ecological vegetation covers, registered cultural heritage sites, and existing land parcel zoning. Group model building is incorporated into the study through eliciting weightings and ratings of importance for each datasets from urban planners to formulate different urban green system scenarios. The second case study focuses on modelling ecoregions from spatial datasets for the state of Queensland. The modelling combines collaborative expert knowledge and a vast amount of environmental data to build biogeographical classifications of regions. An information elicitation process is used to capture expert knowledge of ecoregions as geographical descriptions, and to transform this into prior probability distributions that characterise regions in terms of environmental variables. This prior information is combined with measured data on the environmental variables within a Bayesian modelling technique to produce the final classified regions. We describe how linked views between descriptive information, mapping and statistical plots are used to decide upon representative regions that satisfy a number of criteria for biodiversity and conservation. This paper discusses the advantages and problems encountered when undertaking group model building. Future research will extend the group model building approach to include interested individuals and community groups.