Clinical reasoning performance assessment: using situated cognition theory as a conceptual framework

Developing valid assessment approaches to clinical reasoning performance has been challenging. Situated cognition theory posits that cognition (e.g. clinical reasoning) emerges from interactions between the clinician and situational (contextual) factors and recognizes an opportunity to gain deeper insights into clinical reasoning performance and its assessment through the study of these interactions. The authors apply situated cognition theory to develop a conceptual model to better understand the assessment of clinical reasoning. The model highlights how the interactions between six contextual factors, including assessee, patient, rater, and environment, assessment method, and task, can impact the outcomes of clinical reasoning performance assessment. Exploring the impact of these interactions can provide insights into the nature of clinical reasoning and its assessment. Three significant implications of this model are: (1) credible clinical reasoning performance assessment requires broad sampling of learners by expert raters in diverse workplace-based contexts; (2) contextual factors should be more explicitly defined and explored; and (3) non-linear statistical models are at times necessary to reveal the complex interactions that can impact clinical reasoning performance assessment.

A Data-Weighted Prior Estimator for Forecast Combination

Forecast combination methods reduce the information in a vector of forecasts to a single combined forecast by using a set of combination weights. Although there are several methods, a typical strategy is the use of the simple arithmetic mean to obtain the combined forecast. A priori, the use of this mean could be justified when all the forecasters have had the same performance in the past or when they do not have enough information. In this paper, we explore the possibility of using entropy econometrics as a procedure for combining forecasts that allows to discriminate between bad and good forecasters, even in the situation of little information. With this purpose, the data-weighted prior (DWP) estimator proposed by Golan (2001) is used for forecaster selection and simultaneous parameter estimation in linear statistical models. In particular, we examine the ability of the DWP estimator to effectively select relevant forecasts among all forecasts. We test the accuracy of the proposed model with a simulation exercise and compare its ex ante forecasting performance with other methods used to combine forecasts. The obtained results suggest that the proposed method dominates other combining methods, such as equal-weight averages or ordinal least squares methods, among others.

Position-specific performance profiles, using predictive classification models in senior basketball

Basketball players display different performance characteristics when in different playing positions. Traditional statistical techniques such as Multivariate Analyses of Variance (MANOVA's) are insufficient when predicting specific positions. Alternatively linear statistical models, such as discriminant analysis, have been used. Recently non-linear statistical methods have been introduced into sport science via artificial neural networks that have been proven to have high potential. This study will seek to identify whether artificial neural networks are capable of providing additional insights with regards to the position-specific characteristics found in basketball. A total of 150 Belgian elite players performed physical and physiological tests in the preseason phase. Linear and non-linear predictive models were applied. Discriminant analysis and multi-layer perceptron analysis were able to position, respectively, 92 and 88% of the players correctly. The results of the variable importance analysis demonstrated that the positions clearly differentiated from each other. Herein, weight was the most important factor. Secondly the shuttle run, the speed at anaerobic threshold and the sprint time between 5 and 10 m (respectively, 93.2; 85.0 and 79.5% importance of weight) were important factors. The current study showed that basketball positions clearly differentiate elite Belgian basketball players based solely on basketball independent tests.

Functionality versus dimensionality in psychological taxonomies, and a puzzle of emotional valence

This paper applies evolutionary and functional constructivism approaches to the discussion of psychological taxonomies, as implemented in the neurochemical model Functional Ensemble of Temperament (FET). FET asserts that neurochemical systems developed in evolution to regulate functional-dynamical aspects of construction of actions: orientation, selection (integration), energetic maintenance, and management of automatic behavioural elements. As an example, the paper reviews the neurochemical mechanisms of interlocking between emotional dispositions and performance capacities. Research shows that there are no specific neurophysiological systems of positive or negative affect, and that emotional valence is rather an integrative product of many brain systems during estimations of needs and the capacities required to satisfy these needs. The interlocking between emotional valence and functional aspects of performance appears to be only partial since all monoamine and opioid receptor systems play important roles in non-emotional aspects of behaviour, in addition to emotionality. This suggests that the Positive/Negative Affect framework for DSM/ICD classifications of mental disorders oversimplifies the structure of non-emotionality symptoms of these disorders. Contingent dynamical relationships between neurochemical systems cannot be represented by linear statistical models searching for independent dimensions (such as factor analysis); nevertheless, these relationships should be reflected in psychological and psychiatric taxonomies. This article is part of the theme issue ‘Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences’.

ALGORITHM FOR ESTIMATING OF BREEDING VALUE OF HORSES OF BELARUSIAN HARNESS, RUSSIAN DRAFT AND RUSSIAN TROTTER BREEDS

Algorithms for evaluation of breeding (genetic) values of Belarusian Harness, Russian Draft and Russian Trotter breeds of horses by quantitative traits of own performance (development, expert evaluation of selected traits) of horses and progeny were developed. The theoretical basis for establishment of breeding value of horses by quantitative traits are the linear statistical models, based on which the breeding value is expressed by deviation of trait value of the evaluated animals from the average determined for the breed in our country. The practical significance of the developed system is to improve reliability of horses evaluation and accelerate it for 2–3 years.

Predicting enteric methane emission in sheep using linear and non-linear statistical models from dietary variables

The objective of the present study was to develop linear and non-linear statistical models for prediction of enteric methane emission (EME) in sheep. A database from 80 publications, which included a total of 449 mean observations of EME measured on more than 1500 sheep, was constructed to develop prediction and evaluation of models of EME. Dietary nutrient composition (g/kg), nutrient or energy intake (kg/day or MJ/day) and digestibility (g/kg) of organic matter were used as predictors of EME (MJ/day). The dietary concentrations of neutral detergent fibre and crude protein, and feed intake, were 435 g/kg, 152 g/kg and 0.92 kg/day, respectively. The EME by sheep expressed as MJ/day and % of gross energy intake was 1.02 and 6.54, respectively. The simple linear equation that predicted EME with high precision and accuracy was EME = 0.208(±0.040) + 0.049(±0.0039) × gross energy intake (MJ/day), adjusted R2 = 0.86 with root mean-square prediction error of 22.7%, of which 93% was from random error and regression bias of 3.20%. Additions of dietary concentration of fibre and feeding level, and organic matter digestibility to the simple linear model improved the models. Among the non-linear equations developed, monomolecular model, i.e. EME = 5.699 (±1.94) – [5.699 (±1.94) – 0.133 (±0.047)] × exp[–0.021(±0.0071) × metabolisable energy intake (MJ/day)]; adjusted R2 = 0.90 and mean-square prediction error = 20.1%, with 96.3% random error, performed better than simple linear and other non-linear models. The equations developed in the present study will be useful for national methane inventory preparation, and for a better understanding of dietary factors influencing EME in sheep.