“人体-环境”新系统医学模式探讨

运用系统论观点分析“生物-心理-社会”医学模式的优越性和局限性,提出以人为主体,将人作为一完整系统,人与环境合为大系统,以整体论的健康观为理念的“人体-环境”新系统医学模式,旨在更好地推动中西医结合和预防医学的发展,加速学科间的相互渗透,唤起健康和环保意识,以及更加重视政治和法律政策对现代医学的作用。     

Selection of variables in exploratory factor analysis: An empirical comparison of a stepwise and traditional approach

The purpose of this study was to investigate and compare the performance of a stepwise variable selection algorithm to traditional exploratory factor analysis. The Monte Carlo study included six factors in the design; the number of common factors; the number of variables explained by the common factors; the magnitude of factor loadings; the number of variables not explained by the common factors; the type of anomaly evidenced by the poorly explained variables; and sample size. The performance of the methods was evaluated in terms of selection and pattern accuracy, and bias and root mean squared error of the structure coefficients. Results indicate that the stepwise algorithm was generally ineffective at excluding anomalous variables from the factor model. The poor selection accuracy of the stepwise approach suggests that it should be avoided.     

Indclas: A three-way hierarchical classes model

A three-way three-mode extension of De Boeck and Rosenberg's (1988) two-way two-mode hierarchical classes model is presented for the analysis of individual differences in binary object × attribute arrays. In line with the two-way hierarchical classes model, the three-way extension represents both the association relation among the three modes and the set-theoretical relations among the elements of each model. An algorithm for fitting the model is presented and evaluated in a simulation study. The model is illustrated with data on psychiatric diagnosis. Finally, the relation between the model and extant models for three-way data is discussed.The research reported in this paper was partially supported by NATO (Grant CRG.921321 to Iven Van Mechelen and Seymour Rosenberg).     

Empirical support for a Type A belief system

Much of the research on Type A behavior has focused on overt manifestations of the pattern; consequently, the underlying psychological dimensions are poorly understood (Matthews, 1982). Price (1982) has proposed an elaborate, but empirically unvalidated, model suggesting that a specific set of beliefs underlies the Type A behavior pattern. A series of experiments was conducted to test the validity of Price's model and to develop a device for assessing these beliefs. The internal consistency and test-retest reliability of this measure were .94 and .84, respectively. Significant positive correlations were found with traditional measures of Type A behavior as well as various facets of anger and anxiety. Finally, subjects who endorsed a high degree of Type A beliefs set significantly higher performance standards and were less likely to achieve these standards than their low-scoring counterparts. Results are discussed with regard to the theoretical construct of Type A as well as their implications for future research and treatment.This research is an extension of a study presented at the 92nd meeting of the American Psychological Association, Toronto, Canada, August 1984Special thanks to to Drs. James A. Blumenthal, Martin T. Gipson, Abby C. King, and Ellie T. Sturgis for their helpful comments in the preparation of this article.     

Judgmental forecasting of univariate time series

To forecast numbers appearing in sequence, do people just take some sort of average of past items or do they use temporal pattern information that they have extracted from the sequence? In an experiment, subjects forecast successive numbers generated by a first-order auto regressive algorithm. Afterwards, they were asked to generate their own series of numbers to simulate the sequence they had been forecasting. It was found that (1) generation performance was good—subjects acquired an internal representation of the pattern in the sequence while forecasting it; (2) generation and forecasting performance were uncorrelated—this internal representation was not used for forecasting; (3) learnt ability to forecast a sequence did not transfer to another sequence that was of the same type but that subjects believed to come from another source; (4) subjects were good at estimating the probability that their forecasts would be correct but this ability declined with practice.     

A stochastic multidimensional scaling vector threshold model for the spatial representation of “pick any/n” data

This paper presents a new stochastic multidimensional scaling vector threshold model designed to analyze pick any/n choice data (e.g., consumers rendering buy/no buy decisions concerning a number of actual products). A maximum likelihood procedure is formulated to estimate a joint space of both individuals (represented as vectors) and stimuli (represented as points). The relevant psychometric literature concerning the spatial treatment of such binary choice data is reviewed. The nonlinear probit type model is described, as well as the conjugate gradient procedure used to estimate parameters. Results of Monte Carlo analyses investigating the performance of this methodology with synthetic choice data sets are presented. An application concerning consumer choices for eleven competitive brands of soft drinks is discussed. Finally, directions for future research are presented in terms of further applications and generalizing the model to accommodate three-way choice data.     

Human age classification using facial skin aging features and artificial neural network

In this paper a novel method based on facial skin aging features and Artificial Neural Network (ANN) is proposed to classify the human face images into four age groups. The facial skin aging features are extracted by using Local Gabor Binary Pattern Histogram (LGBPH) and wrinkle analysis. The ANN classifier is designed by using two layer feedforward backpropagation neural networks. The proposed age classification framework is trained and tested with face images from PAL face database and shown considerable improvement in the age classification accuracy up to 94.17% and 93.75% for male and female respectively.     

Decision Pattern Analysis as a General Framework for Studying Individual Differences in Decision Making

This paper investigated decision pattern analysis (DPA) as a general and standard framework for studying individuals' consistent decision making behavior within and between contexts. DPA classifies decisions on the basis of judgement accuracy and the goal orientation of the decided‐upon action. Over repeated decisions, patterns of individuals' decision behavior are described by five variables: competence, optimality, recklessness, hesitancy and decisiveness. A fictitious medical decision making test and three standard cognitive ability tests (extended with confidence ratings and a ‘submit answer for marking’ decision) were used to investigate the psychometric properties of these DPA variables. Internal consistency of the decision patterns ranged from good to excellent. Convergent validity was assessed via cognitive abilities, metacognitive confidence and a control criterion imposed on confidence that determines the decision to be made: the point of sufficient certainty. Personality variables were included to assess discriminant validity. As hypothesised, cognitive abilities showed positive correlations with competence and optimality. High confidence, low points of sufficient certainty and a greater discrepancy between them were associated with higher decisiveness and recklessness, and lower hesitancy. Personality measures showed mixed and generally weak correlations with the DPA variables. These convergent and discriminant results also held after controlling for all variables in regression. The results provide preliminary psychometric support for DPA as a general framework of behavioral decision making. DPA has the potential to be exploited in many contexts for uses that, to date, have been unachievable in a psychometrically valid manner. Copyright © 2015 John Wiley & Sons, Ltd.     

Ⅱ型糖尿病患者的行为特征研究

使用修订行为特征问卷调查了１１２名Ⅱ型糖尿病患者和１４７名健康对照组。结果表明： １．男性患者比健康人更加抑郁;２．女性患者比健康人更加焦虑、抑郁和愤怒;３．不论是男性 还是女性,此问卷可显著地区分这两组,越抑郁的人,越不焦虑的人,越缺少合理化这种心理 防御机制的人,越可能是糖尿病患者。结合其它的研究结果,认为存在着Ⅱ型糖尿病易感性 行为特征,主要表现为：高抑郁;低焦虑;对应激的唤醒水平低;不善于使用心理防御机制来 保护和伪装自己;寻找一些有趣的事情：回避痛苦事件;不善于延迟的满足,要求马上得到满 足;注意易分散等。