医患冲突与诚信的缺失

在新的历史条件下,医患冲突表现出更为复杂的特点,从深层次研究医患冲突的原因,诚信的缺失往往被人们所忽视。多数医疗纠纷源于医患双方缺乏诚信,以自我为中心,过分强调经济利益,缺少人文关怀。必须加强诚信道德教育,建立健全信用法律制度,使诚实守信成为人人自我约束、自觉遵守的行为规范。     

骨折治疗方法与原则的最优化历程

多年来骨折治疗,新旧技术、新旧理论相互融合,相互碰撞,产生了新的理论和方法,使得临床疗效显著提高,现阶段理智的临床医生应该融汇各家之长,多方互补,对每一位骨折患者的治疗都应该按照最优化的原则进行。但是在医疗过程中总存在着如方法选择不当或者错误,过度治疗,医患信任危机,医疗伦理道德的丧失等情况,我们应该用矛盾分析的方法来思考这些问题。     

当代中国医患关系的合理重构——论我国社会转型期医患利益的调节与平衡

我国社会转型 ,为当代中国医患关系的合理重构提供了平台 ,但社会转型期中出现的诸多医患利益冲突也向人们提出了严峻的挑战 ,亟需对其加以调节和平衡。这种调节与平衡 ,不能完全甚至不能主要地交由市场这双“看不见的手”去运作 ,不能完全任凭甚至不能主要地任凭医患双方自由地去博弈 ;政府必须明确并履行自己的职责 ,为正确调节与平衡我国社会转型期的医患利益创立一个全方位的合理的框架 ,使医患之间在有控制的理性的双赢的利益博弈中合理地重构当代中国医患关系的基本格局。     

适宜技术与诊疗最优化的可行性研究

在案例分析的基础上,作出了医生是临床诊疗中适宜技术的主要选择者的判断;论证了运用适宜技术实现诊疗最优化的可行性;认为适宜技术难以选择和实施的主要原因是利益关系、责任心不强和管理措施不到位;提出了必须着力培育适合于适宜技术生长的土壤的对策,即:实行政策引导与资金支持、进一步加大监管力度、营造良好的氛围、高度重视并加强医生道德素质的培养。     

Kindergarten self-control mediates the gender reading achievement gap: A population-based cohort study

Could superior self-control explain the gender difference in reading achievement favoring girls? To test this idea, we drew on a unique population-based sample (N = 11,336) where self-control was measured in kindergarten using a multimethod battery of assessments. Girls showed substantially higher levels of self-control in kindergarten (β = 0.47) and outperformed boys on standardized tests of reading achievement in third/fourth grade (β = 0.20). Further, kindergarten self-control prospectively predicted reading achievement throughout elementary school (β = 0.37). Connecting these findings, our mediation analyses revealed that the female self-control advantage in kindergarten could account for subsequent gender differences in reading achievement. Our results suggest that early gender differences in self-control may represent a key pathway through which gender disparities in reading skills, vocabulary knowledge, and reading comprehension occur.     

Why Higher Working Memory Capacity May Help You Learn: Sampling,Search, and Degrees of Approximation

Algorithms for approximate Bayesian inference, such as those based on sampling (i.e., Monte Carlo methods), provide a natural source of models of how people may deal with uncertainty with limited cognitive resources. Here, we consider the idea that individual differences in working memory capacity (WMC) may be usefully modeled in terms of the number of samples, or “particles,” available to perform inference. To test this idea, we focus on two recent experiments that report positive associations between WMC and two distinct aspects of categorization performance: the ability to learn novel categories, and the ability to switch between different categorization strategies (“knowledge restructuring”). In favor of the idea of modeling WMC as a number of particles, we show that a single model can reproduce both experimental results by varying the number of particles—increasing the number of particles leads to both faster category learning and improved strategy‐switching. Furthermore, when we fit the model to individual participants, we found a positive association between WMC and best‐fit number of particles for strategy switching. However, no association between WMC and best‐fit number of particles was found for category learning. These results are discussed in the context of the general challenge of disentangling the contributions of different potential sources of behavioral variability.     

Cognitive Task Analysis for Implicit Knowledge About Visual Representations With Similarity Learning Methods

Visual representations are prevalent in STEM instruction. To benefit from visuals, students need representational competencies that enable them to see meaningful information. Most research has focused on explicit conceptual representational competencies, but implicit perceptual competencies might also allow students to efficiently see meaningful information in visuals. Most common methods to assess students’ representational competencies rely on verbal explanations or assume explicit attention. However, because perceptual competencies are implicit and not necessarily verbally accessible, these methods are ill‐equipped to assess them. We address these shortcomings with a method that draws on similarity learning, a machine learning technique that detects visual features that account for participants’ responses to triplet comparisons of visuals. In Experiment 1, 614 chemistry students judged the similarity of Lewis structures and in Experiment 2, 489 students judged the similarity of ball‐and‐stick models. Our results showed that our method can detect visual features that drive students’ perception and suggested that students’ conceptual knowledge about molecules informed perceptual competencies through top‐down processes. Furthermore, Experiment 2 tested whether we can improve the efficiency of the method with active sampling. Results showed that random sampling yielded higher accuracy than active sampling for small sample sizes. Together, the experiments provide the first method to assess students’ perceptual competencies implicitly, without requiring verbalization or assuming explicit visual attention. These findings have implications for the design of instructional interventions that help students acquire perceptual representational competencies.