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Too often, employers offer their employees a menu of health insurance alternatives with little guidance other than a description of plan benefits and costs. A multiattribute health insurance choice model can provide employees with needed guidance.
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The aim of the paper is to present the problem, how to find the optimal set of indicators necessary in the different phases of planning, programming and evaluation of health promotion programmes. In health promotion the application of classic health measurements is limited, because they are not related to "positive health". Moreover, time perspective when effects of promotion could appear is difficult to define. Some model approaches are presented. The PRECEDE-PROCEED model is recognised as a robust framework in health promotion. This model provides a continuous series of steps and phases in planning, implementation and evaluation process. Some authors recommend already in the early phase of programming to draw a diagram showing a relationship between the objectives, activities, indicators and resources. In the process of evaluation it seems very important for health promotion initiatives to focus on the changes in human knowledge, attitudes and behaviours. For that reason models like KAP analysis (knowledge, attitudes, practice), HBM (Health Belief Model) and TTM (Transtheoretical Prochaska's model) are commonly applied. However, the- re is still a need to develop and choose indicators, which would consider environmental aspects of health promotion and link local actions with national health promotion policy.
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Explore the source record for details and available documents.
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Departing from a short description of the development of psychiatry in the GDR and the assessment of the present care situation, principles and targets of the further development are outlined on the basis of the result obtained by a research group "Organisation of Psychiatric Care". In the foreground there are the principles of vicinity of the community (decentralisation), the formation of function units of inpatients, semiclinical and outpatient treatment establishments (including the offer of care at sheltered work places, lodgings and during leisure time) for surveyable territories (sectorisation) and the integration of psychiatry in medicine and society.
Models for sexual selection by the coevolution of female preference and a male trait have as a generic feature the existence of a neutrally stable line of equilibria up and down which the system can drift at random. However, this feature is structurally unstable since it is destroyed by introducing either mutation or weak direct selection on female preference into the model, to be replaced by a single equilibrium (or a finite set of disconnected equilibria). This process is investigated in detail under a simple but general population genetic model. It is concluded that the level of female preference is determined by mutation, selection, and genetic drift acting directly on it, and drags the male trait along with it along the line of equilibria. More attention should be paid to selective forces acting directly on female preference.
Models of sexual selection in polygynous species of animals have been derived on the assumption that some females have preferences to mate with males with particular genotypes. The mating advantage gained by the males is always frequency-dependent because the preferred males take part in the same number of preferential matings when they are rare as when they are common; individually therefore, they mate more often when they are rare. Frequency-dependent sexual selection has been demonstrated in many experiments with Drosophila: rare males take part in a higher proportion of matings than their frequency as available mates. Ehrman and Spiess explained this phenomenon by frequency-dependence either in female preference or in male courtship. This explanation, which is difficult to interpret in behavioural terms, may not be necessary, however, because constant female preferences would entail frequency-dependent selection among the males. I show here that a simple model of constant preferences for particular phenotypes or genotypes is sufficient to explain a large body of data on frequency-dependent sexual selection in Drosophila.
Modeling of R.A. Fisher's ideas about the evolution of male ornamentation using quantitative genetics began in the 1980s. Following an initial period of enthusiasm, interest in these models began to wane when theoretical studies seemed to show that the rapid evolution of ornaments would not occur if there were costs associated with female mate choice. Recent theoretical work has shown, however, that runaway evolution and other kinds of extensive diversification of ornaments and preferences can occur, even when female choice is costly. These new models highlight crucial parameters that profoundly influence evolutionary trajectories, but these parameters have been neglected in empirical studies. Here, we review quantitative genetic models of sexual selection with the aim of fostering communication and synergism between theoretical and empirical enterprises. We also point out several areas in which additional empirical work could distinguish between alternative models of evolution.
Two standard mathematical formulations of kin-selection models can be found. Inclusive fitness is an actor-centred approach, which calculates the fitness effect on a number of recipients of the behaviour of a single actor. Direct fitness is a recipient-centred approach, which calculates the fitness effect on the recipient of the behaviour of a number of actors. Inclusive fitness offers us a powerful heuristic, of choosing behaviour to maximize fitness, but direct fitness can be mathematically easier to work with and has recently emerged as the preferred approach of theoreticians. In this paper, we explore the fundamental connection between these two approaches in both homogeneous and class-structured populations, and we show that under simple assumptions (mainly fair meiosis and weak selection) they provide equivalent formulations, which correspond to the predictions of Price's equation for allele frequency change. We use a couple of examples to highlight differences in their conception and formulation, and we briefly discuss a two-species example in which we have a class of 'actor' that is never a 'recipient', which the standard direct fitness method can handle but the usual inclusive fitness cannot.
/ An effective groundwater protection program requires understanding of water flow and contaminant transport processes in the subsurface. Although many mathematical models have been developed to simulate the processes, few actually are used in groundwater protection programs due to the difficulties in data collection, model selection, and model implementation. This study presents a conceptual design of a GIS-supported model selection system that evaluates available data and mathematical models to facilitate groundwater protection programs. Steady-state groundwater and contaminant transport models applied in isotropic aquifers are placed into four classes to simulate conservative or nonconservative contaminant transports in simple or complex geohydrological conditions. After analyzing specific study objectives, available data, and model requirements, the proposed system selects a class of models that can be used in simulation and recommends any need for additional data collection. This study initiates an effort to integrate GIS, mathematical models, and expert knowledge in one system to promote the application of appropriate groundwater models. The new technology of GIS and digital data-base management makes it possible to develop such a system in practice.KEY WORDS: Groundwater models; Geographic information systems
Phylogenetic estimation has largely come to rely on explicitly model-based methods. This approach requires that a model be chosen and that that choice be justified. To date, justification has largely been accomplished through use of likelihood-ratio tests (LRTs) to assess the relative fit of a nested series of reversible models. While this approach certainly represents an important advance over arbitrary model selection, the best fit of a series of models may not always provide the most reliable phylogenetic estimates for finite real data sets, where all available models are surely incorrect. Here, we develop a novel approach to model selection, which is based on the Bayesian information criterion, but incorporates relative branch-length error as a performance measure in a decision theory (DT) framework. This DT method includes a penalty for overfitting, is applicable prior to running extensive analyses, and simultaneously compares all models being considered and thus does not rely on a series of pairwise comparisons of models to traverse model space. We evaluate this method by examining four real data sets and by using those data sets to define simulation conditions. In the real data sets, the DT method selects the same or simpler models than conventional LRTs. In order to lend generality to the simulations, codon-based models (with parameters estimated from the real data sets) were used to generate simulated data sets, which are therefore more complex than any of the models we evaluate. On average, the DT method selects models that are simpler than those chosen by conventional LRTs. Nevertheless, these simpler models provide estimates of branch lengths that are more accurate both in terms of relative error and absolute error than those derived using the more complex (yet still wrong) models chosen by conventional LRTs. This method is available in a program called DT-ModSel.
MOTIVATION: In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'true' prediction error of a prediction model in the presence of feature selection. RESULTS: For small studies where features are selected from thousands of candidates, the resubstitution and simple split-sample estimates are seriously biased. In these small samples, leave-one-out cross-validation (LOOCV), 10-fold cross-validation (CV) and the .632+ bootstrap have the smallest bias for diagonal discriminant analysis, nearest neighbor and classification trees. LOOCV and 10-fold CV have the smallest bias for linear discriminant analysis. Additionally, LOOCV, 5- and 10-fold CV, and the .632+ bootstrap have the lowest mean square error. The .632+ bootstrap is quite biased in small sample sizes with strong signal-to-noise ratios. Differences in performance among resampling methods are reduced as the number of specimens available increase. SUPPLEMENTARY INFORMATION: A complete compilation of results and R code for simulations and analyses are available in Molinaro et al. (2005) (http://linus.nci.nih.gov/brb/TechReport.htm).
Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028 mg/l and a root mean squared error of prediction (RMSEP) of 0.35 mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.