Empathy and professional attitude in social workers and non-trained aides

The purpose of this study was to investigate empathic communication and professional attitude in vocational groups representing different levels of professional training. A further aim was to elucidate some aspects of the impact of the clients. Four groups of professionals participated, three with degrees as social workers and one with only short professional training. Three groups worked with committed drug addicts, one group with somatically ill patients. The 110 subjects were tested with a video-test of professional attitude and empathic communication. Different response patterns were found in the different groups. The group with short training had the highest proportion of unprofessional responses. The group of social workers working with somatically ill patients showed the highest amount of empathic communication. Furthermore, there was a distinction among the kind of unprofessional responses used by the different groups. Hypotheses on the causes of these differences and implications for future research are discussed.     

大学英语写作教学与移情体验

文章从人本主义学习观出发,从研究移情与语言教学的关系入手,通过追踪英语写作教学的发展变化,说明移情在写作教学中起着日益显著的作用.对此,文章从教师和学生两个方面论述了外语写作教学中如何进行移情的问题.     

临床共情的基本路径研究

目的：开展临床共情基本路径研究,调查共情基本路径执行现状,为共情临床实践提供建议。方法：利用自行设计的调查问卷,采用分层随机抽样方法,对江苏省8所公立医院825名临床工作者和在这些医疗机构就诊的807例患者的临床共情基本路径现状进行调查和分析,对医务工作者和患者共情的认知状况进行比较。结果：在临床共情基本路径中的神态、情绪、倾听、微笑环节,医务工作者与患者的认知存在偏差;在耐心、专注、语言、告知、解释环节临床工作者均表现不佳。结论：临床工作者各路径环节表现不佳,需结合临床共情路径的内涵和实施方法,促进临床共情实践的实现和发展。     

江苏省综合性医院临床医生对新医改认知研究

为了解江苏省综合性医院临床医生对新医改现状的看法,评价新医改在江苏省内的实施进展和效果,该研究选取了新医改部分重点问题,采用自设计问卷,调查江苏省内7家三级综合性医院和7家二级综合性医院临床医生,运用描述性分析和卡方检验辅助分析研究结果。发现66.5%的医生更希望政府医改经费投入能侧重于医务人员劳动收入;对家庭医生签约服务的认知度及成效评价低于分级诊疗及医联体建设;认为医患关系和看病难、看病贵问题略有改善的医生分别占20.3%、24.9%、27.9%。需正确认识医务人员认知差异及原因;重视医务人员权益、优化薪酬管理制度;加强家庭医生签约服务的宣传和推广;丰富医联体间活动形式;建立及时有效的临床医生反馈机制。     

A Bayesian sequential design with binary outcome

Several researchers have proposed solutions to control type I error rate in sequential designs. The use of Bayesian sequential design becomes more common; however, these designs are subject to inflation of the type I error rate. We propose a Bayesian sequential design for binary outcome using an alpha‐spending function to control the overall type I error rate. Algorithms are presented for calculating critical values and power for the proposed designs. We also propose a new stopping rule for futility. Sensitivity analysis is implemented for assessing the effects of varying the parameters of the prior distribution and maximum total sample size on critical values. Alpha‐spending functions are compared using power and actual sample size through simulations. Further simulations show that, when total sample size is fixed, the proposed design has greater power than the traditional Bayesian sequential design, which sets equal stopping bounds at all interim analyses. We also find that the proposed design with the new stopping for futility rule results in greater power and can stop earlier with a smaller actual sample size, compared with the traditional stopping rule for futility when all other conditions are held constant. Finally, we apply the proposed method to a real data set and compare the results with traditional designs.     

Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing

Abstract

A central objective of empirical research on treatment response is to inform treatment choice. Unfortunately, researchers commonly use concepts of statistical inference whose foundations are distant from the problem of treatment choice. It has been particularly common to use hypothesis tests to compare treatments. Wald’s development of statistical decision theory provides a coherent frequentist framework for use of sample data on treatment response to make treatment decisions. A body of recent research applies statistical decision theory to characterize uniformly satisfactory treatment choices, in the sense of maximum loss relative to optimal decisions (also known as maximum regret). This article describes the basic ideas and findings, which provide an appealing practical alternative to use of hypothesis tests. For simplicity, the article focuses on medical treatment with evidence from classical randomized clinical trials. The ideas apply generally, encompassing use of observational data and treatment choice in nonmedical contexts.     

Schizotypal Personality Disorder: A Clinical Social Work Perspective

Schizotypal personality disorder (SPD) is considered to be a “schizophrenia spectrum disorder” as evidenced in part by its cross-listing in that chapter of the DSM-5. SPD is considered to be a condition with limited potential for positive change because one of its major features is the presence of a biologically based cognitive deficit. This assumption, however, is an example of the medical model’s creating a bias against psychosocial features that are always involved in character development. The social work profession’s bio-psycho-social perspectives focus more comprehensively on all features of the condition and promote a more optimistic view of clients’ change potentials. The purposes of this paper are to examine SPD from a social work perspective and to demonstrate, with a case example, how effective intervention can be organized and delivered.     

Bayes shrinkage estimator for consistency assessment of treatment effects in multi-regional clinical trials

The primary objective of a multi-regional clinical trial is to investigate the overall efficacy of the drug across regions and evaluate the possibility of applying the overall trial result to some specific region. A challenge arises when there is not enough regional sample size. We focus on the problem of evaluating applicability of a drug to a specific region of interest under the criterion of preserving a certain proportion of the overall treatment effect in the region. We propose a variant of James-Stein shrinkage estimator in the empirical Bayes context for the region-specific treatment effect. The estimator has the features of accommodating the between-region variation and finiteness correction of bias. We also propose a truncated version of the proposed shrinkage estimator to further protect risk in the presence of extreme value of regional treatment effect. Based on the proposed estimator, we provide the consistency assessment criterion and sample size calculation for the region of interest. Simulations are conducted to demonstrate the performance of the proposed estimators in comparison with some existing methods. A hypothetical example is presented to illustrate the application of the proposed method.     

The precision of regression-type estimator for incremental cost–effectiveness ratio

The estimation of incremental cost–effectiveness ratio (ICER) has received increasing attention recently. It is expressed in terms of the ratio of the change in costs of a therapeutic intervention to the change in the effects of the intervention. Despite the intuitive interpretation of ICER as an additional cost per additional benefit unit, it is a challenge to estimate the distribution of a ratio of two stochastically dependent distributions. A vast literature regarding the statistical methods of ICER has developed in the past two decades, but none of these methods provide an unbiased estimator. Here, to obtain the unbiased estimator of the cost–effectiveness ratio (CER), the zero intercept of the bivariate normal regression is assumed. In equal sample sizes, the Iman–Conover algorithm is applied to construct the desired variance–covariance matrix of two random bivariate samples, and the estimation then follows the same approach as CER to obtain the unbiased estimator of ICER. The bootstrapping method with the Iman–Conover algorithm is employed for unequal sample sizes. Simulation experiments are conducted to evaluate the proposed method. The regression-type estimator performs overwhelmingly better than the sample mean estimator in terms of mean squared error in all cases.     

Generalizing the standardized hazard ratio to multivariate proportional hazards regression,with an application to clinical~genomic studies

The standardized hazard ratio for univariate proportional hazards regression is generalized as a scalar to multivariate proportional hazards regression. Estimators of the standardized log hazard ratio are developed, with corrections for bias and for regression to the mean in high-dimensional analyses. Tests of point and interval null hypotheses and confidence intervals are constructed. Cohort sampling study designs, commonly used in prospective–retrospective clinical genomic studies, are accommodated.