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What Aspects of Letters of Recommendation Predict Performance in Medical School? Findings From One Institution

DeZee, Kent J. MD, MPH; Magee, Charles D. MD, MPH; Rickards, Gretchen MD, MPH; Artino, Anthony R. Jr PhD; Gilliland, William R. MD; Dong, Ting PhD; McBee, Elexis DO, MPH; Paolino, Nathalie DO, MPH; Cruess, David F. PhD; Durning, Steven J. MD, PhD

doi: 10.1097/ACM.0000000000000425
Research Reports

Purpose To study medical students’ letters of recommendation (LORs) from their applications to medical school to determine whether these predicted medical school performance, because many researchers have questioned LORs’ predictive validity.

Method A retrospective cohort study of three consecutive graduating classes (2007–2009) at the Uniformed Services University of the Health Sciences was performed. In each class, the 27 students who had been elected into the Alpha Omega Alpha (AOA) Honor Medical Society were defined as top graduates, and the 27 students with the lowest cumulative grade point average (GPA) were designated as “bottom of the class” graduates. For each student, the first three LORs (if available) in the application packet were independently coded by two blinded investigators using a comprehensive list of 76 characteristics. Each characteristic was compared with graduation status (top or bottom of the class), and those with statistical significance related to graduation status were inserted into a logistic regression model, with undergraduate GPA and Medical College Admission Test score included as control variables.

Results Four hundred thirty-seven LORs were included. Of 76 LOR characteristics, 7 were associated with graduation status (P ≤ .05), and 3 remained significant in the regression model. Being rated as “the best” among peers and having an employer or supervisor as the LOR author were associated with induction into AOA, whereas having nonpositive comments was associated with bottom of the class students.

Conclusions LORs have limited value to admission committees, as very few LOR characteristics predict how students perform during medical school.

Dr. DeZee is associate professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Magee is assistant professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Rickards is assistant professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Artino is associate professor of preventive medicine and biometrics, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Gilliland is professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Dong is assistant professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. McBee is assistant professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Paolino is assistant professor of medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Cruess is professor of preventive medicine and biometrics, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Dr. Durning is professor of medicine and pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Funding/Support: This study was supported by an intramural grant from the Dean’s Educational Endowment Fund, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland.

Other disclosures: None reported.

Ethical approval: The authors obtained IRB approval from the Uniformed Services University.

Disclaimer: The views expressed in this paper are those of the authors and do necessarily represent the views of the Uniformed Services University, the Department of Defense, or other federal agencies.

Previous presentations: This research was presented as a poster at the annual meeting of the Society of General Internal Medicine, Denver, Colorado, April 25, 2013.

Correspondence should be addressed to Dr. DeZee, 4301 Jones Bridge Rd.–EDP, Bethesda, MD 20814; telephone: (301) 319-2369; fax: (301) 319-8240; e-mail: kent.dezee@usuhs.edu.

Letters of recommendation (LORs) are a time-honored aspect of the application process for undergraduate and graduate medical training. Despite this long tradition, many researchers and educators have questioned the utility of LORs for this purpose. In his classic article from 30 years ago, “Fantasy land,” Friedman1 has described how LORs are universally inflated and, as a result, become “useless” as predictors of future performance.1

Even with this skepticism, LORs continue to be used for the purpose of medical education selection.2–9 Given their widespread use, it is important to understand the degree to which LORs are a value-added step to the application process, with value being defined as benefit divided by cost. LORs would be beneficial if they could reliably predict educational outcomes, and indeed, two single-institution studies suggest this is possible.10,11 Cullen and colleagues10 showed that the overall rating in the LORs for internal medicine residency applications predicted professionalism scores in internship. Stohl and colleagues11 found that comments about patient care, medical knowledge, and interpersonal and communication skills were more common in the LORs of top- versus bottom-rated obstetrics–gynecology residency graduates. Additional studies also have suggested positive associations with outcomes, but these studies have significant design limitations.12–15

Other authors, however, have found LORs to have no predictive validity.16–19 In terms of cost, the addition of LORs to the application packet is not only taxing for the letter writer but is also a burden on the admissions committee members who must read and interpret each letter. For example, our medical school received 2,778 applications for the 2013 class. If faculty took five minutes to read a single candidate’s LORs, it would take 231 hours to read all of the letters.

Despite the significant investment of time and resources in producing and interpreting LORs, there is limited empirical evidence about how LORs should be used in the selection of medical students, if at all. The purpose of the present study was to determine whether LORs submitted for application to one medical school could predict the students who would become the top and the bottom of the class at graduation. In doing so, we hoped to inform medical school admission committees about how LORs might best be used.

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Method

Participants

We retrospectively studied three consecutive graduating classes (2007–2009) of the Uniformed Services University of the Health Sciences (USU) in Bethesda, Maryland. USU matriculates 170 students per year. In each class, the top 27 students are elected into the Alpha Omega Alpha (AOA) Honor Medical Society, which is approximately 16% of the graduating class, as per AOA regulations; these students embody desirable characteristics beyond grade point average (GPA) such as leadership among peers and professionalism.20 For a comparison group, we designated the 27 students with the lowest cumulative GPA (which includes preclinical and clinical performance) for each class as the “bottom of the class” graduates. We chose this extreme groups approach21 to bolster power and thereby minimize the number of LORs that required coding. For each student, we selected the first three LORs from their medical school application packet. Some packets contained letters that combined LORs from multiple authors into one document. If a section of this document was clearly written by one author, then we considered that section as a single letter for our study. We excluded portions of such documents written by premedical-school committees (sometimes referred to as “committee letters”) because these were infrequent and our goal was to focus solely on the individual-author LOR. As a result of these LOR selection criteria, 10 students in this study had only two LORs.

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Rating form development and rating of LORs

Using the available literature, we created a list of characteristics used in interpreting LORs.10,12,22–29 We then added additional characteristics based on our experience as medical educators interpreting LORs for medical school as well as graduate medical education applications. Additionally, we interviewed five faculty members (who were not otherwise involved with the study) with experience reviewing LORs for medical school applications to delineate additional important characteristics. Next, we consolidated these characteristics to produce a single list, with the goal of including as many characteristics as possible. From this comprehensive list, we developed a data abstraction form that we tested and refined through an iterative process to produce the final form.

The final rating form contained 76 items, 45 of which were based on the literature review. These included 12 general characteristics: total sentence count27,29; number of sentences without the student’s name or person pronoun (assessing for “filler” sentences); number of sentences about the author’s experience evaluating students; presence of typographical or grammatical errors; gender or name errors (i.e., the author copied a previous letter but didn’t change the name/personal pronoun); use of letterhead; use of an official recommendation form; whether the right to see the letter was waived26,27,29; whether the author indicated the student asked him or her to write the letter; number of original sentences that were clearly original (i.e., not adapted from a previous LOR, classified as zero, one, or two or more)22,25,29; whether the reader was invited to contact the author26,29; and whether the contact information was explicitly provided.26,29 We also collected 13 author characteristics12,24,25: academic rank (assistant professor, associate professor, professor)25; graduate medical education position; physician; science professor; nonscience professor; employer/supervisor; nurse; currently military affiliation; relative; friend; or clergy. Next, we assessed 17 student characteristics: specific examples (by name) from outside work/school, exposure to the medical field, and volunteer experience; any comments about intellectual ability (analytic ability, learning ability, etc.),28,29 interpersonal skills (caring, consideration of others, etc.),26–29 and character (conscientious, honesty, maturity, work ethic, etc.)12,26–29; mention of awards (dean’s list, honor roll, etc.); how well the author knew the student (whether or not the word “knew” was used, which was then classified as very well, fairly well, or only slightly); whether the author was enthusiastic to write the letter (e.g., “I’m excited to write this letter for Jane”); the context of the observations (patient care, classroom [including number of courses, course level, and associated lab], and personal life); and whether the observations were mostly from lab experience. We also assessed for 18 comparative rankings: the presence and classification of semiquantitative overall ranking of the student (classified as best, one of the best, better than peers, at the level of peers, and below peers); the presence and the percentile of a quantitative overall ranking; any qualitative or quantitative overall ranking10,22,24,26,27,29; the presence and classification for a denominator for the overall ranking (classified as specific number, estimated denominator, nonspecific, or time span alone)10,22,26; any positive overall description of student (e.g., “Bob is an outstanding student”)24; comparison to graduate students; any comment and explanation ranking the student at the level of peers or below peers27; any other comments that were nonpositive (and explanation); whether the author spontaneously stated that the student would be accepted to his or her own institution; and whether an employer/supervisor spontaneously stated he or she would accept the student for continued employment or promotion in role. For the final category, we abstracted 16 summary characteristics: any “I recommend” statement22,24; the presence of descriptor(s) with the “recommend” statement (absolutely, with confidence, enthusiastically, without hesitancy, highest, highly, in strongest possible terms, without reservation, strongly, unqualified, wholeheartedly)24; whether the descriptor was combined with the term “very”; the number of descriptors; and whether the USU recommendation form was used, and if so, the ranking circled on the USU form (enthusiastically recommend, recommend, recommend with reservations, or do not recommend).

To blind the coders, we deidentified the LORs prior to rating them by removing all names, contact information, and school affiliations; this left only an identification code that could be linked back to the student’s AOA status. Using the rating form, two investigators (K.J.D., C.D.M., or G.R.) independently coded each LOR, with disagreements resolved by consensus. After all coding was complete, we linked the LOR data to each student’s undergraduate GPA, average Medical College Admission Test (MCAT) score,30 and AOA status.

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Analysis

Our null hypothesis was that LOR characteristics would not differ between AOA and bottom of the class students. To ensure an adequate sample size, we wanted at least 75 students per group, which would provide 90% power to detect a difference of 30% or larger between groups, assuming a prevalence of 30% for the characteristic and a P value of < .01. We used each LOR (not the individual student) as the unit of analysis. We chose this approach because many LORs had missing data (e.g., numerical comparative rating), which made creating an average score for each LOR characteristic for each student problematic. For each LOR characteristic, we screened for bivariate association between AOA and bottom of the class students using the chi-square, Fisher exact test, or Student t test as appropriate. We employed a significance level of α = .01, given the multiple comparisons. To control for confounding, we used logistic regression with AOA versus bottom of the class student as the dependent variable and undergraduate GPA (by quartile), average MCAT score (by quartile), and all LOR factors with a P value < .05 on bivariate analysis as the independent variables. We performed a sensitivity analysis on comparative rankings and summary statements using the author’s academic rank (any academic rank versus none and professors versus all others). We used STATA 11.2 statistical software (StataCorp LP; College Station, Texas) for all calculations. The USU institutional review board approved the study.

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Results

We identified 437 LORs (AOA = 214, bottom of the class = 223, Table 1). The average LOR was 18 sentences long (SD 8 sentences, about 1 page long), most were on letterhead, almost half used an official recommendation form, and most students waived their right to see the LOR. Surprisingly, over a third contained a typographical or grammatical error. All general characteristics were statistically similar between AOA and bottom of the class students.

Table 1

Table 1

Slightly more than half of the authors across both groups of students had an academic rank, with roughly half of them being professors (Table 2). Most authors were science professors, and one-third were employers or supervisors. Relatively few were physicians (14%) or nurses (2%). Of all the author characteristics, only employer/supervisor differed between student groups, as AOA students’ LORs were more likely to have been written by an employer/supervisor (36% versus 22%, P = .001).

Table 2

Table 2

Nearly every author commented about the student’s intellectual ability, interpersonal skills, and character (Table 3). Most observations were based on classroom experience. The only student characteristic that was different between student groups was the author description of how well they knew the student, with AOA students more likely than bottom of the class students to be classified as being known “very well” by the author (41% versus 22%, P = .003).

Table 3

Table 3

Most comparative rankings were similar between student groups (Table 4). Quantitative rankings were essentially identical, with the average rank being the top 11th percentile. Authors who were professors or any academic rank were more likely to provide a quantitative comparative ranking, but their ranking did not predict AOA status. AOA students were more likely to be labeled as the “best” (e.g., “X is the best student I have had in ANY course,” 41% versus 17%, P = .01), and this was not different by academic rank status. Although rare (n = 12), LORs in which the employer/supervisor spontaneously stated that he or she would promote or give an expanded role to the applicant were only for AOA students (P = .003). For example, an author wrote about a volunteer teaching aid who would become AOA: “Would I hire X to work with me? Absolutely!” In contrast, bottom of the class students were more likely to have nonpositive comments (13% versus 6%, P = .005). To illustrate, an author wrote about one student who, despite this, went on to become AOA: “His early academic career is spotted with withdrawals and marginal grades.”

Table 4

Table 4

Summary statements, defined as sentences starting with “I recommend,” were of no value in differentiating performance (Table 5). The most common descriptor with “I recommend” was “highly recommend.” Neither the particular descriptor with “I recommend” (absolutely, highly, etc.) nor the number of descriptors was different between student groups. As above, the results were not different for professors or those with any academic rank.

Table 5

Table 5

Three variables remained significant when controlling for undergraduate GPA and MCAT score (Table 6). A semiquantitative rating of “the best” compared with peers (OR 2.5, 95% CI 1.1–5.6, P = .02) and the author being an employer/supervisor (OR 1.9, 95% CI 1.2–3.0, P = .01) were associated with an increased likelihood of graduating AOA. Conversely, having a nonpositive comment (OR 0.47, 95% CI 0.22–1.0, P = .05) decreased the likelihood of graduating AOA. Of note, the “accept for promotion” variable described above could not be included because it was true only in AOA students and therefore was excluded from the logistic regression model, as per regression rules.

Table 6

Table 6

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Discussion

As the medical education community seeks to improve the value of assessment tools in admissions practices, it is important to use tools that can reliably predict outcomes. Students, faculty, and admissions committees, in aggregate, spend a great deal of time requesting, writing, and using LORs, respectively; yet there is no data-driven guidance on how to accomplish this task efficiently and effectively. Our study provides data to inform this process. For students who were accepted to medical school, we have shown that very few aspects of LORs submitted in support of the medical school application packet were associated with whether or not a student graduated at the top of the class. Despite these limited associations, LORs do have some limited value. In particular, our findings suggest that medical school admissions committees should consider giving higher priority to applicants whose LORs rate them as the “best” among their peers, have employer or supervisor authors, and possibly those that include comments about promotion. Further, admissions committees might downgrade, but not necessarily eliminate, applicants with LORs that include nonpositive comments. Although our study thoroughly examined the LORs of students accepted to medical school, we did not examine LORs of students who were rejected. This is an important distinction, and future research might attempt to determine whether the most important purpose of LORs is to reject applicants with overtly negative narratives.

Only one previous report has studied LORs for medical school application, to our knowledge. This single-school study showed that “careful interpretation” of LORs from medical school admissions weakly predicted a variety of preclinical and clinical outcomes, but this study neither controlled for other variables (e.g., GPA) nor was it able to be replicated in the following year.14 Thus, our study is the first to provide any concrete guidance on this important issue.

Previous authors have made recommendations for faculty when writing LORs for graduate medical education,22,26,29,31 but our findings provide only limited support for the characteristics that might differentiate between high- and low-performing students. For example, we attempted to quantify the construct of “depth of understanding” by determining the number of original sentences, which we defined as sentences that clearly could not have been copied from a previous LOR about another student. In practice, this proved difficult to achieve agreement, and we found it did not predict performance. Perhaps another method of determining depth of understanding would be able to show a difference between high and low performers. We also did not find a difference in quantitative numerical ranking, but did find that the top category of semiquantitative ranking, “the best,” was associated with top performers. Lastly, the summary statement of “I recommend” in our study had no predictive validity. It may mean that summary statements need to have a comparison to peers, as others have advocated.26,29,31

Although it is intuitive that the “best” students would go on to become AOA students and that nonpositive comments would be more likely attributed to students at the bottom of the class, we can only speculate as to why having an LOR from an employer or a supervisor is associated with medical school success. Perhaps weaker students spend all of their efforts on their class work and therefore have little time left for outside employment. Another possibility would be that stronger students are able to impress employers or supervisors enough to be hired more often than weaker students. Our finding of a recommendation for “promotion” only in LORs for AOA students supports this theory, as these would presumably be the most impressive students.

Our findings support the notion that readers should not try to search for hidden meanings beyond being labeled “the best,” nonpositive comments, and a recommendation for “promotion.” We found no relationship with performance with other characteristics, such as author enthusiasm in the opening paragraph (e.g., “I am thrilled to write this letter for Jane”), grammatical errors (postulating that an author might subconsciously produce a sloppy letter to indicate a weak student), or the absence of any comparison with peers.

Our findings provide guidance for future research. First, we have shown how LORs might be used to help identify successful students, but we only found one item related to weaker students. Perhaps replication of our work in the context of the entire application packet might help in developing a more robust “scoring system” that could identify these students. Second, if the implications of our study become widely implemented, will applicants and authors try to “game” the system, thereby potentially devaluing the LOR as an admissions tool? Following contents of LORs over time would be useful here, particularly if a standardized LOR for application to medical school comes to fruition.32

The present study had several limitations. First, the data come from a single institution. Second, as USU is a school for military and public health physicians, our applicants may be different from applicants to other medical schools, and LOR authors may have tailored their letters for USU. However, less than 25% of the LOR authors were currently affiliated with the military, making the authors quite similar to those found at other medical schools. Third, we did not attempt to control for other applicant characteristics beyond GPA and MCAT scores. Fourth, it may be that LORs only predict performance on selected attributes (e.g., integrity, resilience, empathy) instead of global performance, though it would be difficult to develop an LOR scoring system for these attributes. Lastly, we did not review the LORs from any rejected applicants. This is an important limitation of our study that restricts the inferences we can make about the value of LORs for admissions decisions.

In conclusion, we found that most characteristics of LORs for medical school application did not predict students’ performance in medical school as measured by top or bottom of the class status. Medical school admissions committees might use LORs written by employers or supervisors and those labeling students as the “best” among peers to rank candidates more strongly. Conversely, students with LORs containing nonpositive comments might be placed lower on the priority list. Future research would be useful to place these aspects in the context of other parts of the admissions packet, to help determine their true predictive power.

Acknowledgments: The authors would like to thank Mr. Allen Kay and Ms. Danielle Fenton for their assistance.

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