Utilizing a Required Documentation Course to Improve the Recording Skills of Undergraduate Social Work Students

ABSTRACT

Baccalaureate social workers respond to documentation and record-keeping demands of regulatory organizations that oversee or fund social service agencies. Learning to accurately document client needs, progress, and service delivery outcomes is important in ensuring that clients are receiving effective services. Despite the importance of documentation skills, many undergraduate social work programs do not provide sufficient curriculum content on client record keeping. The authors report the results of a study that examined changes in baccalaureate social work students’ perceptions of their documentation skills after completing a required documentation course. The sample size included 101 students at pretest and 97 students completing the post-test. Statistically significant findings show students perceived an increase in knowledge in three areas of documentation: 1) formats, 2) content, and 3) writing skills. The authors discuss implications for social work education and practice.     

Log-gamma-generated families of distributions

We introduce two new general families of continuous distributions, generated by a distribution F and two positive real parameters α and β which control the skewness and tail weight of the distribution. The construction is motivated by the distribution of k-record statistics and can be derived by applying the inverse probability integral transformation to the log-gamma distribution. The introduced families are suitable for modelling the data with a significantly skewed and heavy-tailed distribution. Various properties of the introduced families are studied and a number of estimations and data fitness on real data are given to illustrate the results.     

Multivariate testing and model-checking for generalized order statistics with applications

The exponential family structure of the joint distribution of generalized order statistics is utilized to establish multivariate tests on the model parameters. For simple and composite null hypotheses, the likelihood ratio test (LR test), Wald's test, and Rao's score test are derived and turn out to have simple representations. The asymptotic distribution of the corresponding test statistics under the null hypothesis is stated, and, in case of a simple null hypothesis, asymptotic optimality of the LR test is addressed. Applications of the tests are presented; in particular, we discuss their use in reliability, and to decide whether a Poisson process is homogeneous. Finally, a power study is performed to measure and compare the quality of the tests for both, simple and composite null hypotheses.     

On kernel machine learning for propensity score estimation under complex confounding structures

Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive interactions) embedded in these observational data often pose major analytical challenges for proper analysis to draw valid conclusions. Furthermore, often made available as electronic health records (EHRs), these data are usually massive with hundreds of thousands observational records, which introduce additional computational challenges. In this paper, for comparative effectiveness analysis, we propose a statistically robust yet computationally efficient propensity score (PS) approach to adjust for the complex confounding structures. Specifically, we propose a kernel-based machine learning method for flexibly and robustly PS modeling to obtain valid PS estimation from observational data with complex confounding structures. The estimated propensity score is then used in the second stage analysis to obtain the consistent average treatment effect estimate. An empirical variance estimator based on the bootstrap is adopted. A split-and-merge algorithm is further developed to reduce the computational workload of the proposed method for big data, and to obtain a valid variance estimator of the average treatment effect estimate as a by-product. As shown by extensive numerical studies and an application to postoperative pain EHR data comparative effectiveness analysis, the proposed approach consistently outperforms other competing methods, demonstrating its practical utility.     

Projection Mean–Variance Bounds on Expectations of kth Record Values from Restricted Families

We present sharp mean–variance bounds for expectations of kth record values based on distributions coming from restricted families of distributions. These families are defined in terms of convex or star ordering with respect to generalized Pareto distribution. The bounds for expectations of kth record values from DD, DFR, DDA, and DFRA families are special cases of our results. The bounds are derived by application of the projection method.     

CAD电子文件管理流程分析

CAD电子文件管理流程与传统的科技档案管理流程有着显著的差异。这种差异要求我们对CAD电子文件管理流程进行重新的思考和定位。本文从CAD电子文件管理流程的基本指导思想、CAD电子文件管理流程的特点、CAD电子文件管理流程的解析三个角度来阐释其中的差异。     

证人不出庭的逻辑演变与课题展望

在我国,证人不出庭、并代之以庭外证言笔录证明案件事实,经过了立法和司法实践的两重逻辑转变:在立法中,从出庭作证为原则、使用庭外证言笔录为例外,转变为"同等对待"两种方式;在司法中,从两种方式的"同等对待"发展到使用庭外证言笔录为原则、出庭作证为例外。在这种逻辑演变过程中,蕴含着立法、理论基础、诉讼主体的利益需求和博弈等多方面原因,它们是下一步的改革课题。     

“大别山道路”的历史轨迹与当代发展

按照历史与逻辑相统一的方法,分析了安徽农业大学"大别山道路"确立的社会背景、历史功绩、当代社会环境变化和时代责任,并探讨了推进"大别山道路"新征程的各种措施。同时,采用系统理论,从系统的内部结构和外部功能两个方面研究了"大别山道路"的精神内涵。