Communicating risk of medication side effects: an empirical evaluation of EU recommended terminology

Two experiments compared people's interpretation of verbal and numerical descriptions of the risk of medication side effects occurring. The verbal descriptors were selected from those recommended for use by the European Union (very common, common, uncommon, rare, very rare). Both experiments used a controlled empirical methodology, in which nearly 500 members of the general population were presented with a fictitious (but realistic) scenario about visiting the doctor and being prescribed medication, together with information about the medicine's side effects and their probability of occurrence. Experiment 1 found that, in all three age groups tested (18 - 40, 41 - 60 and over 60), participants given a verbal descriptor (very common) estimated side effect risk to be considerably higher than those given a comparable numerical description. Furthermore, the differences in interpretation were reflected in their judgements of side effect severity, risk to health, and intention to comply. Experiment 2 confirmed these findings using two different verbal descriptors (common and rare) and in scenarios which described either relatively severe or relatively mild side effects. Strikingly, only 7 out of 180 participants in this study gave a probability estimate which fell within the EU assigned numerical range. Thus, large scale use of the descriptors could have serious negative consequences for individual and public health. We therefore recommend that the EU and National authorities suspend their recommendations regarding these descriptors until a more substantial evidence base is available to support their appropriate use.

Strengthening argumentation in medical explanations by text planning revision

Explanations are an important by-product of medical decisionsupport activities, as they have proved to favour compliance and correct treatment performance. To achieve this purpose, these texts should have a strong argumentation content and should adapt to emotional, as well as to rational attitudes of the Addressee. This paper describes how Rhetorical Sentence Planning can contribute to this aim: the rulebased plan discourse revision is introduced between Text Planning and Linguistic Realization, and exploits knowledge about the user personality and emotions and about the potential impact of domain items on user compliance and memory recall. The proposed approach originates from analytical and empirical evaluation studies of computer generated explanation texts in the domain of drug prescription. This work was partially supported by a British-Italian Collaboration in Research and Higher Education Project, which involved the Universities of Reading and of Bari, in 1996.

Issues of water supply and contemporary urban society: the case of Greater Amman, Jordan

Over the last two decades, Jordan has suffered a chronic water crisis, and is the tenth most water-scarce nation on Earth. Such water stress has been well illustrated in the case of Greater Amman, the capital, which has grown dramatically from a population of around 2000 in the 1920s, to 2.17 million today. One of the distinctive characteristics of the water supply regime of Greater Amman is that since 1987 it has been based on a system of rationing, with households receiving water once a week for various durations. Amman is highly polarized socio-economically, and by means of household surveys, both quantitative and qualitative, conducted in high- and low-income divisions of the city, a detailed empirical evaluation of the storage and use of water, the strategies used by households to manage water and overall satisfaction with water supply issues is provided in this paper, looking specifically at issues of social equity. The analysis demonstrates the social and economic costs of water rationing and consequent management to be high, as well as emphasizing that issues of water quality are of central importance to all consumers regardless of their socio-economic status within the city.

Key drivers of frontline employee generation of ideas for customer service improvement

Anchored in the service-dominant logic and service innovation literature, this study investigates the drivers of employee generation of ideas for service improvement (GISI). Employee GISI focuses on customer needs and providing the exact service wanted by customers. GISI should enhance competitive advantage and organizational success (cf. Berry et al. 2006; Wang and Netemeyer 2004). Despite its importance, there is little research on the idea generation stage of the service development process (Chai, Zhang, and Tan 2005). This study contributes to the service field by providing the first empirical evaluation of the drivers of GISI. It also investigates a new explanatory determinant of reading of customer needs, namely, perceived organizational support (POS), and an outcome of POS, in the form of emotional exhaustion. Results show that the major driver of GISI is reading of customer needs by employees followed by affective organizational commitment and job satisfaction. This research provides several new and important insights for service management practice by suggesting that special care should be put into selecting and recruiting employees who have the ability to read customer needs. Additionally, organizations should invest in creating work environments that encourage and reward the flow of ideas for service improvement

Mandatory and voluntary information disclosure and the effects on financial analysts: evidence from China

Purpose – This paper aims to make a comparison, different from existing literature solely focusing on voluntary earnings forecasts and ex post earnings surprise, between the effects of mandatory earnings surprise warnings and voluntary information disclosure issued by management teams on financial analysts in terms of the number of followings and the accuracy of earnings forecasts. Design/methodology/approach – This paper uses panel data analysis with fixed effects on data collected from Chinese public firms between 2006 and 2010. It uses an exogenous regulation enforcement to minimise the endogeneity problem. Findings – This paper finds that financial analysts are less likely to follow firms which mandatorily issue earnings surprise warnings ex ante than those voluntarily issue earnings forecasts. Moreover, ex post, they issue less accurate and more dispersed forecasts on former firms. The results support Brown et al.’s (2009) finding in the USA and suggest that the earnings surprise warnings affect information asymmetries. Practical implications – This paper justifies the mandatory earnings surprise warnings policy issued by Chinese Securities Regulatory Commission in 2006. Originality/value – Mandatory earnings surprise is a unique practical regulation for publicly listed firms in China. This paper, for the first time, provides empirical evaluation on the effectiveness of a mandatory information disclosure policy in China. Consistent with existing literature on information disclosure by public firms in other countries, this paper finds that, in China, voluntary information disclosure captures more private information than mandatory information disclosure on corporate earnings ability.

Evaluation of empirical mode decomposition for event-related potential analysis

Current methods for estimating event-related potentials (ERPs) assume stationarity of the signal. Empirical Mode Decomposition (EMD) is a data-driven decomposition technique that does not assume stationarity. We evaluated an EMD-based method for estimating the ERP. On simulated data, EMD substantially reduced background EEG while retaining the ERP. EMD-denoised single trials also estimated shape, amplitude, and latency of the ERP better than raw single trials. On experimental data, EMD-denoised trials revealed event-related differences between two conditions (condition A and B) more effectively than trials lowpass filtered at 40 Hz. EMD also revealed event-related differences on both condition A and condition B that were clearer and of longer duration than those revealed by low-pass filtering at 40 Hz. Thus, EMD-based denoising is a promising data-driven, nonstationary method for estimating ERPs and should be investigated further.

Evaluation of two hybrid metric-conceptual models, for simulating phosphorus transfer from agricultural land in the river enborne, a lowland UK catchment

Across Europe, elevated phosphorus (P) concentrations in lowland rivers have made them particularly susceptible to eutrophication. This is compounded in southern and central UK by increasing pressures on water resources, which may be further enhanced by the potential effects of climate change. The EU Water Framework Directive requires an integrated approach to water resources management at the catchment scale and highlights the need for modelling tools that can distinguish relative contributions from multiple nutrient sources and are consistent with the information content of the available data. Two such models are introduced and evaluated within a stochastic framework using daily flow and total phosphorus concentrations recorded in a clay catchment typical of many areas of the lowland UK. Both models disaggregate empirical annual load estimates, derived from land use data, as a function of surface/near surface runoff, generated using a simple conceptual rainfall-runoff model. Estimates of the daily load from agricultural land, together with those from baseflow and point sources, feed into an in-stream routing algorithm. The first model assumes constant concentrations in runoff via surface/near surface pathways and incorporates an additional P store in the river-bed sediments, depleted above a critical discharge, to explicitly simulate resuspension. The second model, which is simpler, simulates P concentrations as a function of surface/near surface runoff, thus emphasising the influence of non-point source loads during flow peaks and mixing of baseflow and point sources during low flows. The temporal consistency of parameter estimates and thus the suitability of each approach is assessed dynamically following a new approach based on Monte-Carlo analysis. (c) 2004 Elsevier B.V. All rights reserved.

A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

A GIS-based empirical model for vegetation prediction in Lefka Ori, Crete

The aim of the study was to establish and verify a predictive vegetation model for plant community distribution in the alti-Mediterranean zone of the Lefka Ori massif, western Crete. Based on previous work three variables were identified as significant determinants of plant community distribution, namely altitude, slope angle and geomorphic landform. The response of four community types against these variables was tested using classification trees analysis in order to model community type occurrence. V-fold cross-validation plots were used to determine the length of the best fitting tree. The final 9node tree selected, classified correctly 92.5% of the samples. The results were used to provide decision rules for the construction of a spatial model for each community type. The model was implemented within a Geographical Information System (GIS) to predict the distribution of each community type in the study site. The evaluation of the model in the field using an error matrix gave an overall accuracy of 71%. The user's accuracy was higher for the Crepis-Cirsium (100%) and Telephium-Herniaria community type (66.7%) and relatively lower for the Peucedanum-Alyssum and Dianthus-Lomelosia community types (63.2% and 62.5%, respectively). Misclassification and field validation points to the need for improved geomorphological mapping and suggests the presence of transitional communities between existing community types.

Evaluation of data for atmospheric models: master equation/RRKM calculations on the combination reaction, BrO+NO2 -> BrONO2, a conundrum

Experimental data for the title reaction were modeled using master equation (ME)/RRKM methods based on the Multiwell suite of programs. The starting point for the exercise was the empirical fitting provided by the NASA (Sander, S. P.; Finlayson-Pitts, B. J.; Friedl, R. R.; Golden, D. M.; Huie, R. E.; Kolb, C. E.; Kurylo, M. J.; Molina, M. J.; Moortgat, G. K.; Orkin, V. L.; Ravishankara, A. R. Chemical Kinetics and Photochemical Data for Use in Atmospheric Studies, Evaluation Number 15; Jet Propulsion Laboratory: Pasadena, California, 2006)(1) and IUPAC (Atkinson, R.; Baulch, D. L.; Cox, R. A.: R. F. Hampson, J.; Kerr, J. A.; Rossi, M. J.; Troe, J. J. Phys. Chem. Ref. Data. 2000, 29, 167) 2 data evaluation panels, which represents the data in the experimental pressure ranges rather well. Despite the availability of quite reliable parameters for these calculations (molecular vibrational frequencies (Parthiban, S.; Lee, T. J. J. Chem. Phys. 2000, 113, 145)3 and a. value (Orlando, J. J.; Tyndall, G. S. J. Phys. Chem. 1996, 100,. 19398)4 of the bond dissociation energy, D-298(BrO-NO2) = 118 kJ mol(-1), corresponding to Delta H-0(circle) = 114.3 kJ mol(-1) at 0 K) and the use of RRKM/ME methods, fitting calculations to the reported data or the empirical equations was anything but straightforward. Using these molecular parameters resulted in a discrepancy between the calculations and the database of rate constants of a factor of ca. 4 at, or close to, the low-pressure limit. Agreement between calculation and experiment could be achieved in two ways, either by increasing Delta H-0(circle) to an unrealistically high value (149.3 kJ mol(-1)) or by increasing , the average energy transferred in a downward collision, to an unusually large value (> 5000 cm(-1)). The discrepancy could also be reduced by making all overall rotations fully active. The system was relatively insensitive to changing the moments of inertia in the transition state to increase the centrifugal effect. The possibility of involvement of BrOONO was tested and cannot account for the difficulties of fitting the data.

Empirical impact of cache effects for vision system design

Computer vision applications generally split their problem into multiple simpler tasks. Likewise research often combines algorithms into systems for evaluation purposes. Frameworks for modular vision provide interfaces and mechanisms for algorithm combination and network transparency. However, these don’t provide interfaces efficiently utilising the slow memory in modern PCs. We investigate quantitatively how system performance varies with different patterns of memory usage by the framework for an example vision system.

Impacts of pollution and climate change on ombrotrophic Sphagnum species in the UK: analysis of uncertainties in two empirical niche models

A significant challenge in the prediction of climate change impacts on ecosystems and biodiversity is quantifying the sources of uncertainty that emerge within and between different models. Statistical species niche models have grown in popularity, yet no single best technique has been identified reflecting differing performance in different situations. Our aim was to quantify uncertainties associated with the application of 2 complimentary modelling techniques. Generalised linear mixed models (GLMM) and generalised additive mixed models (GAMM) were used to model the realised niche of ombrotrophic Sphagnum species in British peatlands. These models were then used to predict changes in Sphagnum cover between 2020 and 2050 based on projections of climate change and atmospheric deposition of nitrogen and sulphur. Over 90% of the variation in the GLMM predictions was due to niche model parameter uncertainty, dropping to 14% for the GAMM. After having covaried out other factors, average variation in predicted values of Sphagnum cover across UK peatlands was the next largest source of variation (8% for the GLMM and 86% for the GAMM). The better performance of the GAMM needs to be weighed against its tendency to overfit the training data. While our niche models are only a first approximation, we used them to undertake a preliminary evaluation of the relative importance of climate change and nitrogen and sulphur deposition and the geographic locations of the largest expected changes in Sphagnum cover. Predicted changes in cover were all small (generally <1% in an average 4 m2 unit area) but also highly uncertain. Peatlands expected to be most affected by climate change in combination with atmospheric pollution were Dartmoor, Brecon Beacons and the western Lake District.

Impact evaluation of a Kenyan Eco Business: a pilot framework.

Promoting social and environmental entrepreneurship is suggested by many, including international institutions, national development agencies and non-governmental organizations, as critical in tackling longstanding complex global sustainable development problems. However while interest in this kind of alternative entrepreneurship grows, with a proliferation of claims made about its potential to catalyze societal transformation, research in this field remains nascent and fragmented, particularly in relation to Africa. There are few examples of work systematically examining the impacts of social and environmental enterprises on sustainable development and poverty alleviation, especially research based on rigorous empirical fieldwork. This paper begins addressing these limitations by proposing a framework for mapping the sustainable development and poverty alleviation impacts of social and environmental enterprises in Africa. This framework is then piloted with reference to a Kenyan ecobusiness.

An evaluation of decadal probability forecasts from state-of-the-art climate models

While state-of-the-art models of Earth's climate system have improved tremendously over the last 20 years, nontrivial structural flaws still hinder their ability to forecast the decadal dynamics of the Earth system realistically. Contrasting the skill of these models not only with each other but also with empirical models can reveal the space and time scales on which simulation models exploit their physical basis effectively and quantify their ability to add information to operational forecasts. The skill of decadal probabilistic hindcasts for annual global-mean and regional-mean temperatures from the EU Ensemble-Based Predictions of Climate Changes and Their Impacts (ENSEMBLES) project is contrasted with several empirical models. Both the ENSEMBLES models and a “dynamic climatology” empirical model show probabilistic skill above that of a static climatology for global-mean temperature. The dynamic climatology model, however, often outperforms the ENSEMBLES models. The fact that empirical models display skill similar to that of today's state-of-the-art simulation models suggests that empirical forecasts can improve decadal forecasts for climate services, just as in weather, medium-range, and seasonal forecasting. It is suggested that the direct comparison of simulation models with empirical models becomes a regular component of large model forecast evaluations. Doing so would clarify the extent to which state-of-the-art simulation models provide information beyond that available from simpler empirical models and clarify current limitations in using simulation forecasting for decision support. Ultimately, the skill of simulation models based on physical principles is expected to surpass that of empirical models in a changing climate; their direct comparison provides information on progress toward that goal, which is not available in model–model intercomparisons.