ABSTRACT
Objectives
The otolaryngology residency match process is highly competitive. This study evaluated demographic, academic, and linguistic differences in personal statements (PSs) and letters of recommendation (LORs) between matched and unmatched applicants.
Methods
A retrospective quantitative analysis of 2130 residency applicants to one institution during the 2015–2021 match cycles was conducted. Linguistic Inquiry and Word Count 2022 (LIWC2022), a validated software tool, was used to analyze emotional, cognitive, and structural components of written text.
Results
Among 2130 applicants, 1671 (78.5%) matched. Non‐Hispanic White applicants had significantly higher match rates compared to non‐white applicants (p = 0.001). Matched applicants demonstrated superior academic performance, including higher USMLE Step 1 scores (247 ± 12.2 vs. 239.2 ± 16.1, p < 0.001), Step 2 CS pass rates (99.2% vs. 95.8%, p < 0.001), and greater research productivity. Linguistic analysis revealed that matched applicants used more analytical (OR = 1.02, p < 0.001), clout (OR = 1.01, p = 0.007), and positive tone (OR = 1.16, p = 0.003) language in PSs. Unmatched applicants used more negative tone (OR = 0.619, p < 0.001) and negative emotion (OR = 0.54, p = 0.001). LORs for matched applicants exhibited less negative tone (OR = 0.35, p < 0.001).
Conclusion
Matched applicants displayed stronger academic metrics and cognitive language patterns in PSs, highlighting an emphasis on analytical writing over emotional expression. LOR language differed between groups, revealing linguistic features predictive of matching success.
Level of Evidence
III.
Keywords: letters of recommendation, linguistic analysis, otolaryngology, personal statements, residency applications
1. Introduction
Approximately 48,000 medical graduates compete for 40,375 residency positions annually in the United States [1]. Candidates are evaluated based on various academic and clinical criteria, including board scores, letters of recommendation (LORs), Alpha Omega Alpha (AOA) Honor Medical Society status, publications, and personal statements (PSs).
Otolaryngology has consistently ranked among the most competitive specialties in the residency match process, with a match rate of 67% in 2023 [1]. The disproportionately high number of applicants relative to available positions, coupled with the transition of the USMLE Step 1 to a pass/fail scoring system, has made it increasingly difficult for applicants to distinguish themselves. Without a standardized, objective metric like USMLE Step 1 scores, residency programs now rely more heavily on less quantifiable components such as LORs, PSs, research experience, and clerkship performance [2].
Among these factors, LORs have emerged as particularly influential. Residency program directors and chairs frequently cite strong, personalized letters as crucial tools for assessing an applicant's qualifications, interpersonal skills, and readiness for the demands of otolaryngology training [3, 4, 5, 6, 7, 8, 9, 10].
Similarly, PSs are gaining importance, offering applicants a rare opportunity to articulate their motivations, experiences, and suitability for the specialty in their own voice [10, 11, 12]. Despite their growing significance, there is little guidance available to help applicants craft compelling PSs or assist letter writers in creating impactful and effective LORs. This lack of evidence‐based direction leaves both applicants and their mentors navigating these critical components without a clear understanding of what distinguishes a successful application.
To address this gap, our study analyzed the language and content of PSs and LORs, comparing those of matched and unmatched applicants. By identifying patterns and differences, we aim to provide valuable insights that can guide applicants in crafting stronger PSs and inform letter writers on how to better advocate for their candidates. This work represents a critical step toward offering data‐driven guidance in an increasingly competitive and subjective application process.
2. Methods
A retrospective quantitative analysis of 2130 otolaryngology residency applicants to the University of Colorado Otolaryngology Residency Program over the 2015–2021 match cycles was conducted using the Linguistic Inquiry and Word Count 2022 (LIWC‐22) software. LIWC‐22 is a validated tool designed to analyze the emotional, cognitive, and structural components of written text [13].
This study was IRB exempt under COMIRB protocol #22‐1501. The dataset included all applications submitted to the University of Colorado's Otolaryngology Residency Program from 2015 to 2021. Information from each application was parsed using a custom Python program (version 3.10, https://www.python.org), which extracted demographic information, curriculum vitae, PSs, and LORs from PDF files. The text from PSs and LORs was then analyzed using LIWC‐22.
LIWC‐22 includes software and a “dictionary” that maps psychosocial constructs and theories to words, phrases, and other linguistic structures. We assessed four LIWC summary variables: analytical thinking (a metric of logical and formal thinking), clout (language reflecting leadership and status), authenticity (perceived honesty and genuineness), and emotional tone (degree of positive or negative tone) [14].
Match results were determined by reviewing otolaryngology residency program rosters. Applicants not found in rosters were further explored to confirm if they matched into another specialty. Ambiguous cases were excluded.
The Shapiro–Wilk and Anderson–Darling tests confirmed a non‐parametric distribution. The Mann–Whitney test was used to compare LIWC metrics in LORs and PSs based on match status. Categorical data were examined using chi‐square with Fisher's correction, and univariate logistic regression analysis was applied. All statistical analyses were conducted using RStudio (version 2024.04.2 + 764). A p value of < 0.05 was considered significant.
3. Results
A total of 2130 applicants submitted materials during the 2015–2021 match cycles, with an overall match rate of 78.5% (1671/2130). Male applicants accounted for 59% (1265) of the pool, and no significant differences in match rate based on gender were observed. Non‐Hispanic white applicants had higher match rates than non‐white applicants (p = 0.001). Matched applicants had significantly higher rates of positive AOA status than unmatched applicants (92.5% vs. 85.0%, p < 0.0001). Matched applicants also had significantly higher USMLE Step 1 scores (247 ± 12.2 vs. 239.2 ± 16.1, p < 0.001) and Step 2 CS pass rates (99.2% vs. 95.8%, p < 0.001), while unmatched applicants had higher Step 2 CK scores (254.9 ± 11.6 vs. 246.2 ± 14.1, p < 0.001). Matched applicants demonstrated significantly higher numbers of LORs, publications, work experiences, volunteer experiences, and total research activities. Table 1 details applicant demographics.
TABLE 1.
Otolaryngology resident applicant demographics for 2015–2021 application cycles.
| Total (n: 2130) | Matched (n: 1671) (78.45%) | Unmatched (n: 459) (21.54%) | p | ||||
|---|---|---|---|---|---|---|---|
| Mean | Std | Mean | Std | Mean | Std | ||
| Sex | |||||||
| Male | 1265 | 992 (59.3%) | 273 (59.5%) | ||||
| Female | 866 | 680 (40.8%) | 186 (40.5%) | 1.00 | |||
| Nationality | |||||||
| White | 1001 | 826 (82.5%) | 175 (17.5%) | — | |||
| Other | 150 | 108 (72.0%) | 42 (28%) | — | |||
| NA | 240 | 188 (78.3%) | 52 (21.7%) | — | |||
| Asian/AAPI/South Asain | 331 | 257 (77.6%) | 74 (22.3%) | — | |||
| Native American/Alaska Native | 44 | 30 (68.2%) | 14 (31.8%) | — | |||
| Hispanic White | 100 | 71 (71%) | 29 (29%) | — | |||
| Afro Hispanic/Afro‐Carribean | 14 | 11 (73.3%) | 4 (26.7%) | — | |||
| Black/African American | 35 | 26 (74.3%) | 9 (25.7%) | 0.001** | |||
| AOA status | |||||||
| Yes | 1936 (90.8%) | 1546 (92.5%) | 390 (85.0%) | ||||
| No | 195 (9.2%) | 126 (7.5%) | 69 (15.0%) | < 0.0001* | |||
| USMLE | |||||||
| Step 1 | 245.4 | 13.5 | 247 | 12.2 | 239.2 | 16.1 | < 0.0001* |
| Step 2 CK | 252.9 | 12.7 | 246.2 | 14.1 | 254.9 | 11.6 | < 0.0001* |
| Step 2 CS | |||||||
| Pass | 1145 (98.5%) | 892 (99.2%) | 253 (95.8%) | ||||
| Fail | 18 (1.5%) | 7 (0.8%) | 11 (4.2%) | < 0.001* | |||
| Application cycle | |||||||
| 2015–2016 | 312 | 58 (12.6%) | 254 (15.2%) | ||||
| 2016–2017 | 297 | 27 (5.9%) | 270 (16.1%) | ||||
| 2017–2018 | 289 | 29 (6.3%) | 260 (15.6%) | ||||
| 2018–2019 | 410 | 117 (25.5%) | 293 (17.5%) | ||||
| 2019–2020 | 410 | 117 (25.5%) | 293 (17.5%) | ||||
| 2020–2021 | 413 | 111 (24.2) | 302 (18.1%) | < 0.001 | |||
| Number of LOR | 3.8 | 0.4 | 3.9 | 0.3 | 3.8 | 0.4 | 0.017* |
| Number of clinical honors | 2.9 | 5.8 | 2.5 | 2 | 3 | 6.5 | < 0.0001* |
| Number of publications | 4.7 | 4.5 | 4.9 | 4.6 | 3.8 | 3.6 | < 0.0001* |
| Number of presentations | 3.1 | 3.2 | 2.9 | 3 | 3.1 | 3.3 | 0.27 |
| Number of work experience | 3.4 | 2.5 | 3.7 | 2.7 | 3.4 | 2.4 | 0.025* |
| Number of volunteer | 7.9 | 4.3 | 8 | 4.1 | 7.6 | 4.9 | 0.014* |
| Number of posters | 4.2 | 3.9 | 4.3 | 4.2 | 3.5 | 2.9 | 0.27 |
| Number of total research | 4.9 | 2.7 | 5 | 2.6 | 4.9 | 2.7 | < 0.001* |
Note: *indicates statistical significance.
Applicants with higher USMLE Step 1 (OR = 1.04, p < 0.001), Step 2 CK (OR = 1.05, p < 0.001), Step 2 CS pass (OR = 1.05, p < 0.001) scores, higher numbers of LORs (OR = 1.96, p = 0.018), clinical honors (OR = 1.09, p = 0.002), publications (OR = 1.07, p < 0.001), and posters (OR = 1.08, p < 0.001) had higher odds of matching. Additionally, applicants who did not achieve AOA status had significantly lower odds of matching (OR = 0.46, p < 0.001) (Table 2).
TABLE 2.
Odds ratio of applicant characteristics relative to matching success.
| OR | 95% CI | p | |
|---|---|---|---|
| Sex | |||
| Male | 0.9939237 | 0.8046546–1.22575 | 0.96 |
| Female | REF | REF | REF |
| Nationality | |||
| White | REF | REF | REF |
| Other | 0.5447942 | 0.3704887–0.8128106 | 0.002* |
| NA | 0.7659713 | 0.5441156–1.0920629 | 0.002* |
| Asian/AAPI/South Asain | 0.7357994 | 0.5437457–1.0028910 | 0.13 |
| Native American/Alaska Native | 0.4539952 | 0.2400544–0.8990057 | 0.049* |
| Hispanic White | 0.5187025 | 0.3300350–0.8336246 | 0.018* |
| Afro Hispanic/Afro‐Carribean | 0.5826271 | 0.1966821–2.1214860 | 0.005* |
| Black/African American | 0.6120527 | 0.2920850–1.4035302 | 0.36 |
| 0.21 | |||
| AOA status | |||
| Yes | REF | REF | REF |
| No | 0.4606558 | 0.3376057–0.6330156 | < 0.0001* |
| USMLE | |||
| Step 1 | 1.039958991 | 1.032224–1.047897948 | < 0.0001* |
| Step 2 CK | 1.0545421 | 1.0448584–1.0645512 | < 0.0001* |
| Step 2 CS | |||
| Pass | 1.0545421 | 1.0448584–1.0645512 | < 0.0001* |
| Fail | REF | REF | REF |
| Application cycle | |||
| 2015–2016 | REF | REF | REF |
| 2016–2017 | 2.2834643 | 1.4154147–3.7674890 | < 0.001* |
| 2017–2018 | 2.0472441 | 1.2794593–3.3398153 | 0.003* |
| 2018–2019 | 0.571842 | 0.3983300–0.8139363 | 0.002* |
| 2019–2020 | 0.571842 | 0.3983300–0.8139363 | 0.002* |
| 2020–2021 | 0.6212669 | 0.4320096–0.8861982 | 0.009* |
| Number of LOR | 1.9603459 | 1.11222700–3.409967 | 0.0181* |
| Number of Clinical Honors | 1.08881 | 1.033370–1.149215 | 0.002* |
| Number of publications | 1.07254 | 1.042986–1.104796 | < 0.0001* |
| Number of presentations | 1.020935 | 0.9875092–1.058055 | 0.24 |
| Number of work experience | 0.9553956 | 0.9189655–0.9940633 | 0.022* |
| Number of volunteer experiences | 1.018065 | 0.9932988–1.044253 | 0.16 |
| Number of posters | 1.075603 | 1.040160–1.114862 | < 0.0001* |
| Number of total research | 1.061715 | 1.019325–1.10711 | 0.004* |
Note: *indicates statistical significance.
Matched applicants used higher levels of analytical (83.5 ± 9.6 vs. 80.4 ± 11.8, p < 0.001), clout (20.3 ± 11.9 vs. 18.6 ± 10.7, p = 0.038), and positive tone (4.5 ± 1.1 vs. 4.3 ± 1.0, p = 0.004) language in their PSs compared to unmatched applicants. Unmatched applicants used higher levels of authentic language (71.8 ± 17 vs. 69.5 ± 16.3, p = 0.001), negative tone (0.6 ± 0.5 vs. 0.5 ± 0.5, p < 0.001), and negative emotion (0.3 ± 0.3 vs. 0.2 ± 0.3, p = 0.004) (Table 3). LORs for matched applicants exhibited higher levels of positive tone (4.62 ± 1.42 vs. 4.40 ± 1.26, p < 0.001) and positive emotion (1.00 ± 0.41 vs. 0.95 ± 0.38, p = 0.009) (Table 4).
TABLE 3.
LIWC‐22 analysis of differences in personal statements between matched and unmatched applicants.
| LIWC‐22 personal statement | Total (n: 2130) | Matched (n: 1671) (78.45%) | Unmatched (n: 459) (21.54%) | p | |||
|---|---|---|---|---|---|---|---|
| Mean | Std | Mean | Std | Mean | Std | ||
| Analytic | 82.8 | 10.2 | 83.5 | 9.6 | 80.4 | 11.8 | < 0.0001* |
| Clout | 19.9 | 11.7 | 20.3 | 11.9 | 18.6 | 10.7 | 0.038* |
| Authentic | 70 | 16.5 | 69.5 | 16.3 | 71.8 | 17 | 0.001* |
| Tone | 76.8 | 14.4 | 77.6 | 13.9 | 74.1 | 16 | < 0.001 |
| Positive tone | 4.4 | 1 | 4.5 | 1.1 | 4.3 | 1 | 0.004* |
| Negative tone | 0.6 | 0.5 | 0.5 | 0.5 | 0.6 | 0.5 | < 0.0001* |
| Emotion | 1.4 | 0.6 | 1.4 | 0.6 | 1.4 | 0.6 | 0.95 |
| Positive emotion | 1 | 0.5 | 1 | 0.5 | 1 | 0.5 | 0.95 |
| Negative emotion | 0.2 | 0.3 | 0.2 | 0.3 | 0.3 | 0.3 | 0.004* |
Note: *indicates statistical significance.
TABLE 4.
LIWC‐22 analysis of differences in LOR between matched and unmatched applicants.
| LIWC‐22 LOR | Total (n: 2130) | Matched (n: 1671) (78.45%) | Unmatched (n: 459) (21.54%) | p | |||
|---|---|---|---|---|---|---|---|
| Mean | Std | Mean | Std | Mean | Std | ||
| Analytic | 80.27404 | 6.744741 | 80.26658 | 7.52856 | 80.27609 | 6.515546 | 0.23 |
| Clout | 76.87059 | 9.803981 | 77.54285 | 9.313004 | 76.68603 | 9.929246 | 0.12 |
| Authenticity | 5.048902 | 4.341286 | 5.036057 | 3.994445 | 5.052428 | 4.43288 | 0.68 |
| Tone | 81.55138 | 11.25636 | 82.88941 | 10.71071 | 81.18407 | 11.37717 | 0.003* |
| Positive tone | 4.447827 | 1.300937 | 4.61549 | 1.421533 | 4.4018 | 1.262387 | < 0.001* |
| Negative tone | 0.17832 | 0.1554889 | 0.1997603 | 0.1881725 | 0.1724342 | 0.1447472 | 0.07 |
| Emotion | 1.100892 | 0.4102997 | 1.163115 | 0.4436772 | 1.08381 | 0.3990973 | < 0.001* |
| Positive emotion | 0.9587705 | 0.3845823 | 1.0007625 | 0.4076529 | 0.9472428 | 0.3773131 | 0.009* |
| Negative emotion | 0.07212107 | 0.09501226 | 0.0812854 | 0.11420641 | 0.06960526 | 0.08889689 | 0.59 |
Note: *indicates statistical significance.
Applicants with more analytical (OR = 1.02, p < 0.001), clout (OR = 1.01, p = 0.007), and positive tone (OR = 1.16, p = 0.003) writing in their PSs had higher odds of matching. Conversely, applicants with higher levels of negative tone (OR = 0.619, p < 0.001) and negative emotion (OR = 0.54, p = 0.001) in their PSs had lower odds of matching. LORs with higher levels of negative tone (OR = 0.35, p < 0.001) were associated with lower odds of matching (Table 5).
TABLE 5.
Odds ratio for higher success rates of matching into otolaryngology characterized by language characteristics within PSs and LORs.
| OR | 95% CI | p | |
|---|---|---|---|
| LIWC‐22 PS | |||
| Analytic | 1.028 | 1.018–1.038 | < 0.0001* |
| Clout | 1.013 | 1.004–1.022 | 0.007* |
| Authentic | 0.991 | 0.985–0.997 | 0.007* |
| Tone | 1.016 | 1.009–1.023 | < 0.0001* |
| Positive tone | 1.157 | 1.050–1.277 | 0.003* |
| Negative tone | 0.619 | 0.504–0.760 | < 0.0001* |
| Emotion | 1.026 | 0.863–1.222 | 0.772 |
| Positive emotion | 1.203 | 0.971–1.496 | 0.093 |
| Negative emotion | 0.543 | 0.375–0.791 | 0.001* |
| LIWC‐22 LOR | |||
| Analytic | 1.000 | 0.985–1.015 | 0.979 |
| Clout | 1.001 | 0.978–1.026 | 0.943 |
| Authentic | 0.991 | 0.980–1.002 | 0.097 |
| Tone | 0.986 | 0.976–0.995 | 0.004* |
| Positive tone | 0.893 | 0.822–0.962 | 0.004* |
| Negative tone | 0.346 | 0.186–0.652 | < 0.001* |
| Emotion | 0.000 | 0.493–0.808 | < 0.001* |
| Positive emotion | 0.702 | 0.540–0.914 | 0.008* |
| Negative emotion | 0.299 | 0.109–0.842 | 0.020* |
Note: *indicates statistical significance.
4. Discussion
This study highlights key linguistic and academic factors associated with success in the otolaryngology residency match process, offering data‐driven insights into how applicants can refine their applications to improve competitiveness. As residency programs have fewer standardized metrics such as USMLE Step 1 scores to compare applicants, greater emphasis is being placed on subjective measures, including LORs, PSs, research output, and other academic and clinical achievements. Our findings corroborate much of the existing literature on these critical application components and reveal linguistic differences in PSs and LORs that correlate with matching success, providing valuable insights for applicants and letter writers.
Matched applicants demonstrated higher academic metrics, including USMLE Step 1 scores and AOA membership rates; however, the modest odds ratios for these variables (e.g., OR = 1.04 for Step 1, p < 0.001) indicate that academic performance is only one component of a multifactorial evaluation process. AOA membership suggested significant odds ratio improvements with AOA status associated with 2.17 times odds higher of matching into Otolaryngology. This may be due to AOA signifying sustained academic excellence throughout one's medical education as opposed to a single standardized exam in which test taking abilities can hinder one's display of medical knowledge. These findings are consistent with those of Lenze et al., who identified USMLE Step scores, clerkship honors, and research productivity as strong predictors of matching success [8]. Similarly, Lin et al. observed that matched applicants consistently exhibited higher academic metrics and greater research involvement than their unmatched peers, reinforcing the importance of these traditional markers of excellence [10].
Research productivity, as evidenced by higher publication counts and research activities among matched applicants, remains a critical component of applicant evaluation. Smith et al. reported a doubling of research output among otolaryngology applicants over recent years, reflecting the growing importance of academic engagement in this specialty [15]. This trend aligns with our findings, which show that matched applicants demonstrated significantly higher research involvement, with applicants with higher research experiences having 1.06 times higher odds of matching (p < 0.001). These metrics suggest that applicants who invest in scholarly activities are better positioned to meet the expectations of residency programs. The modest odds increase continues to highlight the complexity of the residency application process, where many variables collectively determine outcomes.
In the absence of Step 1 scores as a differentiator, our findings underscore the increasing weight of narrative components, where positive linguistic attributes in PSs and LORs are modest but statistically significant predictors of matching success. This was highlighted by numerous surveys of program directors within different specialties, including Otolaryngology, that characterized the increasing importance of academic rank, LORs, and the personal statement [2, 7, 9, 16].
National data indicate that PSs are used by 74%–78% of programs in the interview selection process, and 48%–54% in final ranking, consistent with our findings that PS emotional, cognitive, and structural components correlate with matching success [17]. This reliance on PSs can provoke anxiety, as one study reported over 80% of residency applicants expressed anxiety about writing their PS [18].
Jones et al. emphasized that a well written PS allows applications to articulate their motivations and suitability for a program in their own voice [19]. Hinkle et al. noted that PDs value PSs that reveal personal attributes not evident elsewhere in the application, helping programs assess an applicant's fit and curate personalized interview experiences [20]. Landry et al. concluded that effective PSs are concise, specific, and reflective of an applicant's unique perspective [11]. Our study highlights that linguistic features such as analytical thinking, positive tone, and emotions were associated with matching success, likely a reflecting an applicant's academic ability and enthusiasm for their chosen specialty. Our study found an analytical tone (OR = 1.02, p < 0.001) and positive tone (OR = 1.16, p = 0.003) modestly increase the odds of matching, while negative tone in PSs (OR = 0.619, p < 0.001) significantly reduce the likelihood of success. Interestingly, it is also unknown whether applicants with more positive tone in their PSs possess other qualities, such as superior interviewing skills, that could further impact their chances of matching. Exploring this potential link would provide valuable insights into how narrative components influence an applicant's overall evaluation.
LORs also played a critical role in distinguishing matched from unmatched applicants. Rajesh et al. and Saudek et al. highlighted the importance of strong, enthusiastic LORs that emphasize specific applicant strengths, including work ethic, leadership, and collaboration [6, 21]. Additionally, program directors consistently interpreted enthusiastic and positive language in LORs as indicative of strong candidates, influencing their impressions during evaluations [6, 21]. Our findings align with these observations, as LORs for matched applicants were characterized by a more positive tone, a feature known to influence PD perceptions and candidate rankings. Our study found that applicants with LORs with a more negative tone had 0.35 times lower odds of matching into Otolaryngology (p < 0.001). Kong et al. noted that LORs with high levels of enthusiasm and well‐structured endorsements carry significant weight in applicant evaluations, further supporting the importance of the tone and content of these letters [22].
Taken together, these findings emphasize the need for applicants to excel across both traditional metrics and narrative components to remain competitive. While factors such as AOA membership, Step scores, and research productivity are key indicators of academic readiness, the tone and structure of PSs and LORs offer valuable, and increasingly critical, insight into personal qualities and overall program fit. However, the growing emphasis on narrative elements warrants careful consideration of the potential unintended consequences. As more applicants and letter writers adopt stylistic strategies associated with success—such as analytical or positively toned language—there may be a drift toward homogenization. This could reduce the authenticity and uniqueness of application materials, potentially limiting their effectiveness in differentiating candidates.
Furthermore, implicit biases may inadvertently be reinforced if evaluators come to favor certain linguistic styles. Applicants from underrepresented backgrounds or non‐native English speakers may express themselves using diverse narrative patterns that do not align with these emerging norms. Consequently, what is perceived as ‘effective’ writing may disadvantage otherwise qualified applicants who convey their strengths differently. Future research should investigate how linguistic expectations impact equity in the holistic review process and explore whether standardized LORs or structured PS guidance could help preserve both individuality and fairness in residency selection.
5. Limitations
While our study provides valuable insights and represents 83% of the total applicant pool to Otolaryngology (2130/2560, total applicants obtained from NRMP data over the 6 year study period), it is limited by the use of a single institution's dataset, which may not fully represent broader trends across otolaryngology residency programs. Additionally, although linguistic analysis tools like LIWC offer robust quantitative data, they cannot capture the nuanced context and subtleties of written language in PSs and LORs. Furthermore, our study does not differentiate between standardized and non‐standardized LORs, which may provide additional insights into the qualities of effective recommendations. Lastly, obtaining match results manually through the review of otolaryngology residency program rosters may have introduced inaccuracies, as some websites were not consistently updated. Future studies should explore these areas further and incorporate qualitative methods to provide a more comprehensive understanding of the content and structure of these critical narrative components.
6. Conclusion
As residency programs increasingly emphasize holistic reviews, applicants must strategically approach every component of their application. Our findings reinforce the importance of excelling in traditional metrics while crafting compelling PSs and securing strong LORs. These elements, when thoughtfully executed, can significantly enhance an applicant's competitiveness in the evolving landscape of otolaryngology residency selection.
Disclosure
Institution Where Work Was Done: University of Colorado Anschutz Medical Campus, Department of Otolaryngology–Head and Neck Surgery, Aurora, CO, USA.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding: The authors received no specific funding for this work.
References
- 1. National Resident Matching Program , “Results and Data: 2023 Main Residency Match. NRMP,” https://www.nrmp.org.
- 2. Wang A., Karunungan K. L., Story J. D., Ha E. L., and Braddock C. H., “Residency Program Director Perspectives on Changes to US Medical Licensing Examination,” JAMA Network Open 4, no. 10 (2021): e2129557, 10.1001/jamanetworkopen.2021.29557. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Fan R. R., Aziz F., Wittgen C. M., Williams M. S., and Smeds M. R., “A Survey of Vascular Surgery Program Directors: Perspectives Following USMLE Step 1 Conversion to Pass/Fail and Virtual Only Interviews,” Annals of Vascular Surgery 88 (2023): 32–41, 10.1016/j.avsg.2022.08.020. [DOI] [PubMed] [Google Scholar]
- 4. Kalyoussef E., Dhanda A. K., and Paskhover B., “Implications of Change to USMLE Step 1 Scoring for Otolaryngology Residency Programs,” Otolaryngology and Head and Neck Surgery 163, no. 5 (2020): 855–856, 10.1177/0194599820936631. [DOI] [PubMed] [Google Scholar]
- 5. Pittman C. A., Standiford T. C., and Bowe S. N., “Otolaryngology Residency Selection: Are We Doing It Right?,” Current Opinion in Otolaryngology & Head and Neck Surgery 29, no. 6 (2021): 517–525, 10.1097/moo.0000000000000760. [DOI] [PubMed] [Google Scholar]
- 6. Rajesh A., Rivera M., Asaad M., et al., “What Are We Really Looking for in a Letter of Recommendation?,” Journal of Surgical Education 76, no. 6 (2019): e118–e124, 10.1016/j.jsurg.2019.06.008. [DOI] [PubMed] [Google Scholar]
- 7. Goshtasbi K., Abouzari M., Tjoa T., Malekzadeh S., and Bhandarkar N. D., “The Effects of Pass/Fail USMLE Step 1 Scoring on the Otolaryngology Residency Application Process,” Laryngoscope 131, no. 3 (2021): E738–E743, 10.1002/lary.29072. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Lenze N. R., Mihalic A. P., DeMason C. E., et al., “Predictors of Otolaryngology Applicant Success Using the Texas STAR Database,” Laryngoscope Investigative Otolaryngology 6, no. 2 (2021): 188–194, 10.1002/lio2.549. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Mamidi I. S., Gu A., Mulcahy C. F., Wei C., and Zapanta P. E., “Perceived Impact of USMLE Step 1 Score Reporting to Pass/Fail on Otolaryngology Applicant Selection,” Annals of Otology, Rhinology and Laryngology 131, no. 5 (2022): 506–511, 10.1177/00034894211028436. [DOI] [PubMed] [Google Scholar]
- 10. Lin M. E., Kharidia K., Choe D., Bagrodia N., Luu N. N., and Chambers T., “A Comparison of Otolaryngology Residency Applicants Over Time and to Other Surgical Applicants,” OTO Open 8, no. 1 (2024): e115, 10.1002/oto2.115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Landry A., Coates W. C., and Gottlieb M., “Creating a High‐Quality Personal Statement for Residency Application: A Guide for Medical Students and Mentors,” AEM Education and Training 6, no. 5 (2022): 10797, 10.1002/aet2.10797. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Moulton M., Lappé K., Raaum S. E., Milne C. K., and Chow C. J., “Making the Personal Statement ‘Truly Personal’: Recommendations From a Qualitative Case Study of Internal Medicine Program and Associate Program Directors,” Journal of Graduate Medical Education 14, no. 2 (2022): 210–217, 10.4300/JGME-D-21-00849.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13. Tausczik Y. R. and Pennebaker J. W., “The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods,” Journal of Language and Social Psychology 29, no. 1 (2010): 24–54, 10.1177/0261927X09351676. [DOI] [Google Scholar]
- 14. Boyd R. L., Ashokkumar A., Seraj S., and Pennebaker J. W., The Development and Psychometric Properties of LIWC‐22 (University of Texas at Austin, 2022), https://www.liwc.app. [Google Scholar]
- 15. Smith D. H., Zeitouni J., Kim Thiesse N., and Bowe S. N., “Trends of Rising Research Production Among Otolaryngology Residency Applicants,” OTO Open 8, no. 3 (2024): 170, 10.1002/oto2.170. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Strausser S. A., Dopke K. M., Groff D., Boehmer S., and Olympia R. P., “Importance of Residency Applicant Factors Based on Specialty and Demographics: A National Survey of Program Directors,” BMC Medical Education 24, no. 1 (2024): 275, 10.1186/s12909-024-05267-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Naples R., French J. C., Lipman J. M., Prabhu A. S., Aiello A., and Park S. K., “Personal Statements in General Surgery: An Unrecognized Role in the Ranking Process,” Journal of Surgical Education 77, no. 6 (2020): e20–e27, 10.1016/j.jsurg.2020.03.021. [DOI] [PubMed] [Google Scholar]
- 18. Campbell B. H., Havas N., Derse A. R., and Holloway R. L., “Creating a Residency Application Personal Statement Writers Workshop: Fostering Narrative, Teamwork, and Insight at a Time of Stress,” Academic Medicine 91, no. 3 (2016): 371–375, 10.1097/ACM.0000000000000863. [DOI] [PubMed] [Google Scholar]
- 19. Jones D., J. R. Pittman, Jr. , and Manning K. D., “Ten Steps for Writing an Exceptional Personal Statement,” Journal of Graduate Medical Education 14, no. 5 (2022): 522–525, 10.4300/JGME-D-22-00331.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Hinkle L., Carlos W. G., Burkart K. M., McCallister J., and Bosslet G., “What Do Program Directors Value in Personal Statements? A Qualitative Analysis,” ATS Scholar 1, no. 1 (2020): 44–54, 10.34197/ats-scholar.2019-0004OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Saudek K., Saudek D., Treat R., Bartz P., Weigert R., and Weisgerber M., “Dear Program Director: Deciphering Letters of Recommendation,” Journal of Graduate Medical Education 10, no. 3 (2018): 261–266, 10.4300/JGME-D-17-00712.1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Kong J. H., Steele L. J., and Botham C. M., “Ten Simple Rules for Writing Compelling Recommendation Letters,” PLoS Computational Biology 17, no. 2 (2021): e1008656, 10.1371/journal.pcbi.1008656. [DOI] [PMC free article] [PubMed] [Google Scholar]