The Significance of Reading between Lines
Letters and evaluations play a significant role in higher education and career progression. This includes applications or letters for training programs, scholarships and grants, awards and recognition, leadership roles, new job opportunities, promotions and tenure, and performance evaluations relating to all of the above. Application letters are frequently used in “Round 1” selection, even before a candidate interviews for a position. This means that such letters and evaluations—and the language used to describe a candidate—can significantly, even if unintentionally, influence the candidate’s consideration. In turn, language that draws from implicit biases can also influence the candidate’s standing. In this article, we briefly summarize types of biased language that can appear in support letters or performance evaluations, and highlight opportunities and resources to mitigate them.
Implicit bias is a type of bias that arises from unconscious associations and stereotypes about members of a social group. Often, bias is based on gender, race/ethnicity, ability, language proficiency, or any aspect of one’s identity. Gendered language usage occurs in medicine, health care, and in professions and areas beyond our usual areas as physicians: the World Bank noted in a 2019 report that, “Attitudes toward women are also influenced by gendered languages… gendered languages could translate into outcomes like lower female labor force participation.”1
Common Terms Related to Bias by Gender
Gendered terms are words that are used to associate with a specific gender. Various studies have noted that gendered language appears in the following:
- letters of recommendation for academic faculty,science and medicine;2
- subjective evaluation for students applying to residency programs;3
- qualitative evaluations of residents and students;4 and
- student, resident, and fellow evaluations of faculty physicians.5
The table provides a brief summary of common gendered terms in letters. Per Trix et al the adjective ‘successful’ occurred in 7% vs 3% of letters for men and women, respectively, while the nouns ‘accomplishment’ and ‘achievement’ occurred in 13% vs 3% of the letters for men and women, respectively. For women applicants “compassionate” and “relates well to patients and staff at all levels” stood out (16% vs 4% in letters for women and men, respectively).6
Ross et al reported that white applicants are more likely to be described with standout words (e.g., outstanding, exceptional, best) when compared to Blacks, Asians, and Hispanics; white applicants are also more likely to be described as “bright” and “organized.” Women are more likely than men to be described with words related to compassion, and they are also more likely to be described as “bright” and “organized.” “Competent” was the only descriptor used more frequently for Blacks than any other race/ethnic group, and additional contextual analysis implied it was used as a word of minimal assurance when describing Black and Hispanic trainees.7
Raising Doubt, Hedging Language, and Faint Praise
Doubt-raising language includes negative, potentially negative, hedging, unexplained, irrelevant comments, and faint praise. In a study by Trix et al, 24 % vs 12% of the letters written for female vs male applicants had at least one doubt raiser (p-value 0.01).6 Examples of negative or potentially negative comments include: “while she has not done,” “while not the best student I have had,” and “bright, enthusiastic, he responds well to a minimum amount of supervision.” Examples of hedging include: “it appears that” or “now that she has chosen,” and an example of faint praise is “she worked hard on projects that she enjoys.”
Implicit Bias Affects Faculty and Supervisors
Disparities in academic job achievements and academic promotions are widespread, especially affecting faculty identifying as women, persons of color, and/or those identifying as LGBTQ+ persons. Faculty and supervisors are not protected from similar effects in terms of biased language. Evaluations from physician trainees play a critical role in promotion decisions and awards for medical faculty in academic medicine. Furthermore, letters and evaluations using implicitly biased language could lead to high rates of attrition at multiple points along the promotion pathway.
Sheffield et al evaluated gender-based differences in the assessment of GIM faculty by trainees in inpatient and outpatient settings. Their study noted Female GIM faculty received lower overall teaching scores than their male counterparts in the inpatient setting. In the inpatient setting, males received higher ratings vs their female peers in overall teaching and across all competencies. However, in the outpatient setting, females received higher ratings vs male faculty, with no difference in ratings for overall teaching and across all competencies.8
Meanwhile, Heath et al reported gendered words are used frequently in faculty evaluations. Their study found that quantitative linguistic differences in free-text comments based on faculty gender persisted after adjustment for evaluator gender and level of training. Furthermore, the use of ability terms (such as master and complexity) was associated with evaluations of men, while the use of emotive terms (such as empathetic, delight, and warm) was associated with the evaluation of women faculty members.5
Where Do We Go from Here?
As a Division, Practice, or Health System
Implicit bias training can serve as an essential foundation for recognizing that language and the ways in which it is used can perpetuate discrimination and bias. A review of the use of letters or evaluations in advancement and ensuring the weight of the language is balanced with an objective measure of performance is helpful. Lastly, engaging in open dialogue among leadership and learners about language and the use of biased language may lead to organic solutions, customized for the local environment.
As a Candidate Requesting an Evaluation or Letter
Sponsors must be able to discuss an applicant’s best skills and greatest professional accomplishments. In circumstances where candidates can choose their own letter or evaluation writers, candidates should strongly consider only asking for letters from sponsors who would describe them as excellent or outstanding candidates. Those who write a letter must be well-positioned to provide the necessary information with a sufficient perspective on the candidate’s measures of performance, and do so in a convincing manner, using unbiased language. The best quality letters usually come from sponsors who genuinely believe that the candidate is the best fit for the position, promotion, grant, award, or other targeted pursuit.
As an Evaluator or Letter Writer
Focus on the applicant as an outstanding candidate—include comments about the commitment and relationship of the writer to the candidate. Dedicate the appropriate length of the text to describe the applicant’s record, and give specific examples of excellence. Focus on evaluating the accomplishments of the applicant. Use of first names should be approached with caution to avoid triggering unconscious biases, even though the intention is to convey a sense of knowing the person well or reducing the appearance of being a generic letter. Beware of and avoid using doubt-raising language, stereotyping, gendered language, and discussion of personal characteristics (unless they predict potential growth and job performance). Consider using a free online bias checker to help. See the extended reference material and links to free online tools that are linked at the end of this article.
We offer readers the full bibliography for this article and additional reading and resources online. Raising awareness of these biases is the first step in addressing them. The second step is to mitigate the use of such language by choosing appropriate evaluators, focusing on the accomplishments of candidates, and leveraging technology (i.e. online decoders). Each of us has a role (as candidates and evaluators) in mitigating these biases. Thus, we are aspiring to a new reality when reading between the lines will no longer be necessary.
- For the full reference list and resources, please see: Leung TI, Sagar A, Henry TL, Shroff S. SGIM2022: Recognizing and Reducing Bias in Letters of Support and Performance Evaluations in 360 Degrees. SGIM 2022 Annual Meeting. Presentation. Figshare. https://doi.org/10.6084/m9.figshare.22093343.v1.
Career Development, Health Equity, Medical Education, SGIM, Social Justice
Dr. Sagar (Ankita.Sagar@CommonSpirit.org; Twitter: @sagar_ankita) is the system vice president for clinical standards & variation reduction at CommonSpirit Health and associate clinical professor of medicine at Creighton University School of Medicine. Dr. Henry (email@example.com; Twitter: @docwithapurpose) is an associate professor of medicine in the department of medicine at Emory University School of Medicine. Dr. Shroff (firstname.lastname@example.org) is the director of the Jefferson Women’s Resident Clinic and associate clinical professor of Medicine at Thomas Jefferson University. Dr. Leung (Editor.SocietyGIMForum@gmail.com; Twitter: @TleungMD) is the editor-in-chief of SGIM Forum.
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