道德困境研究的范式沿革及其理论价值

通过评述道德困境研究范式的发展过程, 系统阐释了经典两难法、加工分离法、CNI模型法和CAN算法的优缺点和理论价值。后来的研究范式均在一定程度上克服了之前研究范式的局限。加工分离法克服了经典两难法的加工纯粹性假设等局限, CNI模型法在加工分离法基础上进一步分离了道德困境决策的多种心理过程, CAN算法则修正了CNI模型法的序列加工的不恰当预设。研究范式的沿革启示研究者综合应用新方法来解决研究争议和重新审视以往道德理论, 合理应用新方法来探索其他具有潜在冲突性的研究议题。总之, 本文为道德困境及相关研究提供了方法学参考。     

Gaussian variational estimation for multidimensional item response theory

Multidimensional item response theory (MIRT) is widely used in assessment and evaluation of educational and psychological tests. It models the individual response patterns by specifying a functional relationship between individuals' multiple latent traits and their responses to test items. One major challenge in parameter estimation in MIRT is that the likelihood involves intractable multidimensional integrals due to the latent variable structure. Various methods have been proposed that involve either direct numerical approximations to the integrals or Monte Carlo simulations. However, these methods are known to be computationally demanding in high dimensions and rely on sampling data points from a posterior distribution. We propose a new Gaussian variational expectation--maximization (GVEM) algorithm which adopts variational inference to approximate the intractable marginal likelihood by a computationally feasible lower bound. In addition, the proposed algorithm can be applied to assess the dimensionality of the latent traits in an exploratory analysis. Simulation studies are conducted to demonstrate the computational efficiency and estimation precision of the new GVEM algorithm compared to the popular alternative Metropolis–Hastings Robbins–Monro algorithm. In addition, theoretical results are presented to establish the consistency of the estimator from the new GVEM algorithm.     

Communication of Information about Genetic Risks: Putting Families at the Center

Genetic information is a family affair. With the expansion of genomic technologies, many new causal genes and variants have been established and the potential for molecular diagnoses increased, with implications not only for patients but also their relatives. The need for genetic counseling and intrafamilial circulation of information on genetic risks grew accordingly. Also, the amount and, particularly, the complexity of the information to convey multiplied. Sharing information about genetic risks with family members, however, has never been an easy matter and often becomes a source of personal and familial conflicts and distress. Ethical requisites generally prevent healthcare professionals from directly contacting their consultands' relatives (affected or still at risk), who often feel unsupported throughout that process. We discuss here the communication of genetic risks to family members. We first consider genomic testing as a basis for family‐centered health care, as opposed to a predominant focus on the individual. We reviewed the literature on sharing genetic risk information with family members, and the associated ethical issues for professionals. Some clinical cases are presented and discussed, and key issues for meeting the needs of individuals and families are addressed. We argue that genetic information is inextricably linked to the family and that communicating about genetic risks is a process grounded within the broader milieu of family relationships and functioning. We conclude for the need for a more family‐centered approach and interventions that can promote sensitive attitudes to the provision of genetic information to and within the family, as well as its inclusion in educational and training programmes for genetic healthcare professionals.     

Numerical approximation of the observed information matrix with Oakes' identity

An efficient and accurate numerical approximation methodology useful for obtaining the observed information matrix and subsequent asymptotic covariance matrix when fitting models with the EM algorithm is presented. The numerical approximation approach is compared to existing algorithms intended for the same purpose, and the computational benefits and accuracy of this new approach are highlighted. Instructive and real-world examples are included to demonstrate the methodology concretely, properties of the estimator are discussed in detail, and a Monte Carlo simulation study is included to investigate the behaviour of a multi-parameter item response theory model using three competing finite-difference algorithms.     

如何利用开源工具分析fMRI数据：基于多体素模式的有监督机器学习算法介绍

摘要：目前,多体素模式分析（MVPA）日渐普遍地应用于脑影像研究。近些年,机器学习的模式分类等算法在MVPA方法中被广泛应用,因其具有能够抽取高维数据模式,提高数据利用率的优点。其中一种典型的应用是利用解码的思想来解决神经表征问题,本文主要介绍了利用基于Python语言的工具库中有监督学习算法分析数据的过程。除介绍Nilearn结合Scikit-learn分析数据的步骤外,还比较不同算法的效率,为算法的选择及参数设备提供具体参考。     

Association rule learning in neuropsychological data analysis for Alzheimer’s disease

Efficient methods of analysis readily available for clinicians continue to be limited within neuropsychological assessment at the raw data level. Here, a novel approach for generating predictive response patterns and analysing neuropsychological raw data is offered. In order to assess the usefulness of association rule learning as an analysis tool for neuropsychological raw data, Frequent Pattern Growth (FP-Growth) was used to mine patterns from the Consortium to Establish a Registry for Alzheimer’s Disease Neuropsychological Battery (CERAD-NB) database. Complete assessment data for 84 post-mortem confirmed Alzheimer’s disease (AD) cases and 294 age, race, and education matched controls were analysed across baseline and one-year follow-up using FP-Growth, for the purpose of assessing the clinical utility of a finer analysis at the raw data level and the feasibility of predicting response patterns for clinical/control groups. Output from FP-Growth, in terms of the number of frequent itemsets retained across both evaluation timepoints, was discernable between controls, mild, and moderate to severe Alzheimer’s disease cases (p < .001, and η² = .488). Patterns within raw data scores, both in terms of frequent itemsets and predictive association rules, appear to be differentiable across groups within neuropsychological analysis, which indicates that FP-Growth is applicable as a supplementary analysis tool for neuropsychological assessment by means of offering an additional level of data analysis, predictive item response capabilities, and aiding in clinical decision making.     

Genetic counselling: Predicting patient outcomes

Abstract

With the rapid development of genetic testing, the demand for genetic counselling is increasing. Yet there is little known about what makes for effective or efficient genetic counselling. The aim of the current study is to determine the main factors influencing outcomes of genetic consultations. One hundred and thirty one routine consultations conducted in a regional genetics centre were audiotaped, transcribed and coded. The main predictors of outcome tested were pre-interview patient and counsellor expectations; pre-interview patient concerns; length of consultations; counsellor directiveness; and the extent to which social and emotional issues are addressed in the consultation. The main outcomes were the patient's view of the extent to which their expectations were met, their satisfaction with information provided, and concern about the problem they were referred with.

Although there were six significant correlations between the process of consultation and outcome variables, multivariate analyses revealed no significant predictors of any of the outcomes. Using multiple regression analyses, small amounts of variance in patient outcomes were predicted by patient and counsellor variables assessed before the consultation, but none was predicted by any of the process measures made of the consultation. Various explanations are considered to explain the lack of influence of process variables upon outcomes. One possible explanation for these findings is that the consultation has a minimal effect upon patient outcomes. Alternatively, the study, in design or analysis, was not sufficiently sensitive to detect the influential aspects of the consultation.     

Intention to Participate in Predictive Genetic Testing for Hereditary Cancer: The Role of Attitude toward Uncertainty

This study aimed to identify factors predictive of intentions to participate in genetic testing for hereditary cancer. Measures of the psychosocial influences on intention were based on the theories of reasoned action (TRA) and planned behaviour (TPB) and a new measure of Attitude toward Uncertainty. The participants (the breast cancer sample, n = 124; the colon cancer sample, n = 168) were recruited via a general practitioner's patient database and asked to complete postal questionnaires. The overall response rate was 57.4% (54.7% in the colon cancer sample, 61.4% in the breast cancer sample). In the colon cancer sample, 72% of respondents stated that they would choose to participate in genetic testing for colon cancer susceptibility. In the breast cancer sample, 77% of respondents stated that they would choose to participate in genetic testing for breast cancer susceptibility. The TRA components (attitudes and subjective norms) and Attitude toward Uncertainty emerged as the strongest predictors of intention. The Attitude toward Uncertainty appears to moderate intention in that participants with more negative attitudes toward uncertainty are more likely to undergo the test than those seeking more certainty.