Coordination meetings as a means of fostering collective learning among jury members involved in the validation of prior learning (VPL)

Jury members involved in the validation of prior learning (VPL) are supposed to draw parallels between the skills the candidate has acquired and the criteria established in relation to the frame of reference. The recalibration of criteria is a form of collective work. During meetings, jury members face common obstacles together. They try to sidestep the prescribed work and then collectively take ownership of it. Collective meetings allow responsibilities to be shared. This contrast between the imposition of extremely narrow prescribed work and the possibility of modifying prescribed work during collective meetings, sets a dialogic process in motion. Such a process gives jury members the opportunity to evolve in their practices. This ensures that jury members are always in a position to reflect on their own methods of action.     

Development and Application of a Probabilistic Risk–Benefit Assessment Model for Infant Feeding Integrating Microbiological,Nutritional, and Chemical Components

A probabilistic and interdisciplinary risk–benefit assessment (RBA) model integrating microbiological, nutritional, and chemical components was developed for infant milk, with the objective of predicting the health impact of different scenarios of consumption. Infant feeding is a particular concern of interest in RBA as breast milk and powder infant formula have both been associated with risks and benefits related to chemicals, bacteria, and nutrients, hence the model considers these three facets. Cronobacter sakazakii, dioxin‐like polychlorinated biphenyls (dl‐PCB), and docosahexaenoic acid (DHA) were three risk/benefit factors selected as key issues in microbiology, chemistry, and nutrition, respectively. The present model was probabilistic with variability and uncertainty separated using a second‐order Monte Carlo simulation process. In this study, advantages and limitations of undertaking probabilistic and interdisciplinary RBA are discussed. In particular, the probabilistic technique was found to be powerful in dealing with missing data and to translate assumptions into quantitative inputs while taking uncertainty into account. In addition, separation of variability and uncertainty strengthened the interpretation of the model outputs by enabling better consideration and distinction of natural heterogeneity from lack of knowledge. Interdisciplinary RBA is necessary to give more structured conclusions and avoid contradictory messages to policymakers and also to consumers, leading to more decisive food recommendations. This assessment provides a conceptual development of the RBA methodology and is a robust basis on which to build upon.     

Promoting social equality values to improve judgement of a woman reporting sexism / El fomento de valores relacionados con la igualdad social para mejorar la opinión sobre las mujeres que denuncian discriminación por sexismo

Previous research has shown that discriminated women blame themselves more than they blame discrimination when meritocracy values are salient. In two studies, we examined whether meritocracy values also influence female observers when they judge a female victim of sexism. Such values were expected to lead them to judge more positively a victim incriminating herself than a victim claiming discrimination. Conversely, social equality values should lead them to judge more positively a victim claiming discrimination. Women who were either feminists or non-feminists (Study 1) or who were exposed to either social equality values or personal merit values (Study 2) had to judge a female victim of sexism who ascribed what happened to discrimination or to her ability. Feminist women and women exposed to social equality judged the female victim more positively when she reported discrimination than when she incriminated herself. The reverse pattern of judgement was observed for non-feminist women and women exposed to meritocracy values. The importance of values is discussed to improve the image of women claiming sexism.     

Nonlinear mixed effects modelling for estimation of steady state attainment

Assessment of the time needed to attain steady state is a key pharmacokinetic objective during drug development. Traditional approaches for assessing steady state include ANOVA‐based methods for comparing mean plasma concentration values from each sampling day, with either a difference or equivalence test. However, hypothesis‐testing approaches are ill suited for assessment of steady state. This paper presents a nonlinear mixed effects modelling approach for estimation of steady state attainment, based on fitting a simple nonlinear mixed model to observed trough plasma concentrations. The simple nonlinear mixed model is developed and proposed for use under certain pharmacokinetic assumptions. The nonlinear mixed modelling estimation approach is described and illustrated by application to trough data from a multiple dose trial in healthy subjects. The performance of the nonlinear mixed modelling approach is compared to ANOVA‐based approaches by means of simulation techniques. Copyright © 2005 John Wiley & Sons, Ltd.     

Asymptotics of cross-validated risk estimation in estimator selection and performance assessment

Risk estimation is an important statistical question for the purposes of selecting a good estimator (i.e., model selection) and assessing its performance (i.e., estimating generalization error). This article introduces a general framework for cross-validation and derives distributional properties of cross-validated risk estimators in the context of estimator selection and performance assessment. Arbitrary classes of estimators are considered, including density estimators and predictors for both continuous and polychotomous outcomes. Results are provided for general full data loss functions (e.g., absolute and squared error, indicator, negative log density). A broad definition of cross-validation is used in order to cover leave-one-out cross-validation, V-fold cross-validation, Monte Carlo cross-validation, and bootstrap procedures. For estimator selection, finite sample risk bounds are derived and applied to establish the asymptotic optimality of cross-validation, in the sense that a selector based on a cross-validated risk estimator performs asymptotically as well as an optimal oracle selector based on the risk under the true, unknown data generating distribution. The asymptotic results are derived under the assumption that the size of the validation sets converges to infinity and hence do not cover leave-one-out cross-validation. For performance assessment, cross-validated risk estimators are shown to be consistent and asymptotically linear for the risk under the true data generating distribution and confidence intervals are derived for this unknown risk. Unlike previously published results, the theorems derived in this and our related articles apply to general data generating distributions, loss functions (i.e., parameters), estimators, and cross-validation procedures.