基于系统动力学的“互联网+健康医疗”体系模型

“互联网+健康医疗”模式已然成为我国健康医疗产业发展的一个新方向,为解决传统医疗行业中存在的诸多问题提供了新思路。在分析“互联网+健康医疗”对传统医疗的影响及其发展机制的基础上,利用存量流量图等工具构建“互联网+健康医疗”体系模型,运用Vensim软件对我国“互联网+健康医疗”发展情况进行仿真,并模拟相关政策的干预效果。结果表明:“互联网+健康医疗”在降低潜在医疗需求(PMD)、促进分级诊疗中具有重要作用,保障医疗保险能够较好地嵌入“互联网+健康医疗”就诊流程中对降低PMD最有意义,而降低整体门诊价格对于优化就医结构、促进分级诊疗最为有效。     

An improved comorbidity summary score for measuring disease burden and predicting mortality with applications to two national cohorts

Abstract

Research involving administrative healthcare data to study patient outcomes requires the investigator to account for the patient’s disease burden in order to reduce the potential for biased results. Here we develop a comorbidity summary score based on variable importance measures derived from several statistical and machine learning methods and show it has superior predictive performance to the Elixhauser and Charlson indices when used to predict 1-year, 5-year, and 10-year mortality. We used two large Veterans Administration cohorts to develop and validate the summary score and compared predictive performance using the area under ROC curve (AUC) and the Brier score.     

Improving patients’ satisfaction in a mobile hospital using Lean Six Sigma – a design-thinking intervention

Abstract

Lean Six Sigma (LSS) that offers a unique integration of Lean and Six Sigma has been accepted globally across services sectors. Previous studies that examined the application of LSS in healthcare were steered in traditional hospital settings. Apparently, there are no studies that highlight the application of LSS in mobile hospitals, an emerging trend in the healthcare sector. The aim of this article is to explore the applicability of LSS in a mobile hospital. The authors present a case study of improving patients’ satisfaction in a mobile hospital, through reducing turnaround time. Design-thinking intervention to LSS helped resolve a real-time problem using Define-Measure-Analyze-Design-Verify roadmap. This article contributes to the ‘healthcare quality” body of knowledge and is novel as it is the first of its kind of LSS application in a mobile hospital underpinning Design thinking. This study is unique as it demonstrates academia–industry collaboration in solving an organizational problem.     

A Hitchhiker's Guide to caring for an older person before and during coronavirus‐19

Coronavirus‐19 (COVID‐19) has reconfigured working lives with astonishing velocity. Older people have suffered the worst effects of the pandemic, with governments marginalizing or overlooking their needs. Women perform the majority of care for older people, often compromising their working lives and health. Yet in academic articles their voices are often filtered or aggregated in quantitative studies. Based on a weave of personal experiences and secondary research, the article traces a path through UK forms of care and shows how the inadequate response to COVID‐19 stemmed from existing policies embedded in health and social care. COVID‐19 has severed important informal care work, rendering the vulnerable yet more exposed and carers anxious and bereft. Longitudinal research capturing the trajectory of care from the perspective of older people and their carers would lead to improved support hence gender equality.     

幼儿园教师虐童行为的特征、影响及对策

幼儿园教师虐童事件数次发生，已经成为全社会普遍愤慨和共同忧虑的焦点问题。幼儿园教师虐童具有隐蔽性强，儿童难以抗拒，施虐方式多样，越小儿童越易受到虐待等表现特征。虐童行为不仅伤及儿童身体，更给儿童心理和精神造成严重危害。为有效防止幼儿园教师虐童行为，需要从政府、幼儿园和家长层面采取具有针对性的防治措施。     

国外手机依赖研究综述

目前国外对手机依赖的概念尚未达成共识；在手机依赖研究对象的选取上主要以青年群体为主，方法也主要采用定量研究；考察了手机依赖和性别、年龄、自尊等八个变量之间的关系；手机依赖的危害涉及生理、心理和社会三个方面，对手机依赖的防范主要从手机本身和教育两方面着手。国外对手机依赖的研究主要从生理和心理的微观层面进行，还缺乏宏观层面的考察。     

The relations between maternal mental state talk and preschoolers’ behavioral adaptation and school readiness: moderation by emotion situation knowledge

This study investigated the extent to which associations between mothers' elaborated talk about mental states and preschoolers’ behavioral adaptation (i.e., social competence and internalizing and externalizing behavior) and school readiness were moderated by emotion situation knowledge. Families (N = 120) were mostly middle‐income and White and 70 of the preschoolers (M = 50.65 months, SD = 6.19) were boys. Results revealed a positive association between elaborated maternal mental state talk and social competence, but only for children average and high in emotion situation knowledge. For children low in emotion situation knowledge, there was a positive association between elaborated maternal mental state talk and internalizing behavior. There also was a negative relation between elaborated maternal mental state talk and school readiness for preschoolers low in emotion situation knowledge. Findings highlight the importance of considering emotion situation knowledge when examining associations between elaborated maternal mental state talk and young children’s social behavioral adaptation and readiness for school.     

基于行为系统模型的农村中学英语教师专业发展策略研究

新的英语课程标准将“发展学生的英语学科核心素养”作为根本任务，这对英语教师的专业化水平提出了更高的要求。源于社会生态系统理论的行为系统模型，可为教师专业发展研究提供科学的思考框架。在深入分析行为系统模型的理论缘起、内涵结构基础上，全面梳理了现阶段农村中学英语教师专业发展状况以及存在的问题，从微系统、中系统、外系统、宏系统四个层次分别阐述了推动农村中学英语教师专业发展的策略，对保障英语课程标准的贯彻落实、中学英语教学质量的提升等具有一定的价值和意义。     

Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods

ABSTRACT

The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a ‘library’ of candidate prediction models. While SL has been widely studied in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of SL in its ability to predict the propensity score (PS), the conditional probability of treatment assignment given baseline covariates, using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also proposed a novel strategy for prediction modeling that combines SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases.