Department of Surgery, School of Medicine, University of Virginia, Charlottesville, VA.
School of Nursing, University of Virginia, Charlottesville, VA; Data Science Institute, University of Virginia, Charlottesville, VA.
J Am Coll Surg. 2019 Apr;228(4):356-365.e3. doi: 10.1016/j.jamcollsurg.2018.12.020. Epub 2019 Jan 8.
Implicit bias has been documented in candidate selection within academic medicine. Gender bias is exposed when writers systematically use different language to describe attributes of male and female applicants. This study examined the presence of gender bias in recommendation letters for surgical residency candidates.
Recommendation letters for 2016 to 2017 surgery resident applicants selected for interview at an academic institution were analyzed using qualitative text analysis, quantitative text mining, and topic modeling. Dedoose, QDA Miner, and RStudio analytic software were used for analysis.
There were 332 letters of recommendation for 89 applicants (51% male) analyzed. Of 265 letter writers, 86% were male, 21% chairs, and 50% professors. Average word count was 404. Letter writers for male compared with female applicants had a significantly higher average word count (male = 421, SD 144; female = 388, SD 140, p = 0.035). Standout adjectives (eg exceptional), reference to awards, achievement, ability, hardship, leadership, scholarship, and use of applicant's name were most often applied to male applicants. Comments on positive general terms (eg delightful), grindstone words (eg hard-working), physical description, doubt raisers, and work ethic were most often applied to female applicants. Topic modeling and term frequencies revealed achievement words (performance, career, leadership, and knowledge) used more often with male applicants, while caring words (care, time, patients, and support) were used more often with female applicants.
Gendered differences examined through language and text exist in surgical residents' recommendation letters. Implementing tools to help faculty write recommendation letters with meaningful content and editing letters for reflections of stereotypes may improve the resident selection process by reducing bias.
在学术医学领域的候选人选拔中,已经记录到了隐性偏见。当作者系统地使用不同的语言来描述男性和女性申请人的特征时,就会暴露性别偏见。本研究检查了在外科住院医师候选人的推荐信中是否存在性别偏见。
使用定性文本分析、定量文本挖掘和主题建模分析了在学术机构接受面试的 2016 年至 2017 年外科住院医师申请人的推荐信。使用 Dedoose、QDA Miner 和 RStudio 分析软件进行分析。
对 89 名申请人(51%为男性)的 332 封推荐信进行了分析。在 265 位写信人中有 86%为男性,21%为主席,50%为教授。平均字数为 404 个。与女性申请人相比,男性申请人的推荐信的平均字数明显较高(男性=421,SD 144;女性=388,SD 140,p=0.035)。杰出的形容词(如杰出)、提及奖项、成就、能力、困难、领导力、奖学金和申请人姓名的使用最常应用于男性申请人。对积极的一般术语(如愉快)、磨石词(如勤奋)、身体描述、质疑和职业道德的评论最常应用于女性申请人。主题建模和术语频率显示,与男性申请人相关的推荐信中更常使用成就相关的词(表现、职业、领导力和知识),而与女性申请人相关的推荐信中更常使用关心相关的词(关心、时间、病人和支持)。
通过语言和文本检查到的外科住院医师推荐信中的性别差异。实施帮助教师撰写具有有意义内容的推荐信并编辑信件以反映刻板印象的工具,可能通过减少偏见来改善住院医师选拔过程。