J. Rubenstein is associate professor, Department of Pediatrics, Baylor College of Medicine, Houston, Texas.
S. Rahiem is a clinical fellow in neonatology, University of Washington, Seattle, Washington.
Acad Med. 2023 Jul 1;98(7):800-804. doi: 10.1097/ACM.0000000000005171. Epub 2023 Jun 23.
Microaggressions are pervasive in daily life, including in undergraduate and graduate medical education and across health care settings. The authors created a response framework (i.e., a series of algorithms) to help bystanders (i.e., health care team members) become upstanders when witnessing discrimination by the patient or patient's family toward colleagues at the bedside during patient care, Texas Children's Hospital, August 2020 to December 2021.
Similar to a medical "code blue," microaggressions in the context of patient care are foreseeable yet unpredictable, emotionally jarring, and often high-stakes. Modeled after algorithms for medical resuscitations, the authors used existing literature to create a series of algorithms, called Discrimination 911, to teach individuals how to intervene as an upstander when witnessing instances of discrimination. The algorithms "diagnose" the discriminatory act, provide a process to respond with scripted language, and subsequently support a colleague who was targeted. The algorithms are accompanied by training on communication skills and diversity, equity, and inclusion principles via a 3-hour workshop that includes didactics and iterative role play. The algorithms were designed in the summer of 2020 and refined through pilot workshops throughout 2021.
As of August 2022, 5 workshops have been conducted with 91 participants who also completed the post-workshop survey. Eighty (88%) participants reported witnessing discrimination from a patient or patient's family toward a health care professional, and 89 (98%) participants stated that they would use this training to make changes in their practice.
The next phase of the project will involve continued dissemination of the workshop and algorithms as well as developing a plan to obtain follow-up data in an incremental fashion to assess for behavior change. To reach this goal, the authors have considered changing the format of the training and are planning to train additional facilitators.
微侵犯行为在日常生活中普遍存在，包括本科和研究生医学教育以及整个医疗保健环境。作者创建了一个应对框架（即一系列算法），以帮助旁观者（即医疗团队成员）在目睹患者或患者家属在床边照顾患者时对同事的歧视时，从旁观者转变为挺身而出者，这是在 2020 年 8 月至 2021 年 12 月期间在德克萨斯儿童医院进行的。
类似于医疗“代码蓝”，在患者护理背景下的微侵犯行为是可预见但不可预测的、情绪上令人震惊的，而且往往是高风险的。作者借鉴了医疗复苏的算法模型，利用现有文献创建了一系列算法，称为“歧视 911”，以教导个人在目睹歧视行为时如何作为挺身而出者进行干预。这些算法“诊断”歧视行为，提供用脚本语言做出回应的过程，随后支持被针对的同事。这些算法伴随着关于沟通技巧和多样性、公平性和包容性原则的培训，通过一个 3 小时的研讨会进行，包括授课和迭代角色扮演。该算法于 2020 年夏季设计，并在 2021 年通过试点研讨会进行了改进。
截至 2022 年 8 月，已进行了 5 次研讨会，共有 91 名参与者参加，他们还完成了研讨会后的调查。80 名（88%）参与者报告说目睹了患者或患者家属对医疗保健专业人员的歧视，89 名（98%）参与者表示他们将利用这项培训在实践中做出改变。
该项目的下一阶段将包括继续推广研讨会和算法，以及制定一项计划，以逐步获得后续数据来评估行为变化。为了实现这一目标，作者考虑改变培训形式，并计划培训更多的 facilitators。
