Visual Causal Models in Clinical Explanation

临床解释中的视觉因果模型

• 批准号: 7895781
• 负责人: NANCY S. KIM
• 金额: $ 7.8万
• 依托单位: NORTHEASTERN UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2012-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7895781
• 关键词: Adult; Agreement; American; Attention; Belief; Belief System; Characteristics; Client; Clinical; Cognitive; Communication; Companions; Complex; Comprehension; Computer software; Data; Disease; Disease model; Education; Educational process of instructing; Effectiveness; Ensure; Future; Goals; Health; Health Status; Hospitals; Investigation; Knowledge; Laboratories; Learning; Left; Literature; Medicine; Memory; Mental Health; Modeling; Outpatients; Persons; Preparation; Research; Software Tools; Testing; Time; Treatment Protocols; Visual; Visual Aid; Work; clinical application; clinical practice; computer program; computerized; cost; design; forgetting; health literacy; improved; mental representation; open source; programs; public health relevance; research study; response; theories

DESCRIPTION (provided by applicant): One of the mental health clinician's daily challenges is to adequately explain disorders and prescribed treatment regimens to clients of varying levels of health literacy while optimizing clients' comprehension and memory in a time-limited situation. We propose to test whether computerized visual causal models can assist clinicians in more effectively transmitting information about disorders and their treatments to clients. In accord with recent research indicating that dual-mode presentations reduce the learner's cognitive load (Ginns, 2005; Mayer, 2001; Reed, 2006), we predict that when a visual causal model is paired with a verbal explanation, it will help enhance client understanding and increase retention of clinical information. Our proposed study of lay people and mental health clients will examine their understanding and recall after listening to a clinician's verbal explanation versus listening to the same explanation accompanied by a visual causal model. Our experiment makes use of a new software program that we have developed to allow clinicians to easily create and display causal models in clinical practice. This program, entitled Conceptbuilder, and a companion piece of software, Conceptanalysis, have been grounded in current cognitive theory regarding the mental representation and use of explanatory knowledge (Kim, 2005). We suggest that this software will be useful both for improving clinical practice and for facilitating research on the effectiveness with which clinicians explain disorders and treatments to clients of varying levels of health literacy. PUBLIC HEALTH RELEVANCE: People who cannot understand what their doctor tells them about their disorder or treatment generally tend to have poorer health (IOM, 2004). This application draws upon past research in education and learning to test whether using visual aids, created by a computer program developed in our lab, can help doctors explain clinical information more clearly to their clients. The program itself has been made freely available to the public for use in research or clinical settings.

描述（由申请人提供）：心理健康临床医生的日常挑战之一是充分解释疾病和规定的治疗方案，以不同水平的健康素养的客户，同时优化客户的理解和记忆在一个时间有限的情况。我们建议测试计算机化的视觉因果模型是否可以帮助临床医生更有效地将有关疾病及其治疗的信息传递给客户。根据雅阁的最新研究表明，双模式演示减少了学习者的认知负荷（Ginns，2005; Mayer，2001; Reed，2006），我们预测，当视觉因果模型与口头解释配对时，它将有助于增强客户的理解和增加对临床信息的保留。我们提出的研究外行人和心理健康的客户将检查他们的理解和回忆后，听临床医生的口头解释与听同样的解释伴随着一个视觉因果模型。我们的实验利用了我们开发的一种新的软件程序，使临床医生能够在临床实践中轻松创建和显示因果模型。这个程序，名为Conceptbuilder，和一个配套的软件，Conceptanalysis，已经扎根于当前的认知理论，关于心理表征和解释性知识的使用（Kim，2005）。我们建议，该软件将是有用的，既为改善临床实践和促进研究的有效性，临床医生解释疾病和治疗的不同水平的健康素养的客户。公共卫生相关性：不了解医生对他们的疾病或治疗的描述的人通常健康状况较差（IOM，2004）。该应用程序借鉴了过去在教育和学习方面的研究，以测试使用由我们实验室开发的计算机程序创建的视觉辅助工具是否可以帮助医生更清楚地向客户解释临床信息。该计划本身已免费提供给公众用于研究或临床环境。