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Record W4385581038 · doi:10.1097/jw9.0000000000000099

Socioeconomic factors and financial burdens of research “gap years” for dermatology residency applicants

2023· article· en· W4385581038 on OpenAlexaboutno aff
Audrey A. Jacobsen, Gowri Kabbur, Rebecca Freese, Katelyn J. Rypka, Noah Goldfarb

Bibliographic record

VenueInternational Journal of Women’s Dermatology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsFamily medicineSpecialtyMedicineInstitutional review boardAccreditationSocioeconomic statusGraduate medical educationDemographicsDermatologyMedical educationDemographyEnvironmental health

Abstract

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What is known about this subject in regard to women and their families? There is a paucity of available literature regarding the burdens of “gap years” specific to female dermatology residency applicants. What is new from this article as messages for women and their families? Our study indicates a potential gender bias in the match application process. We found that female dermatology residents, when controlling for marital status, showed a higher tendency to take a gap year compared to male residents. However, caution is required when interpreting this finding due to the small sample size, limited response rate, and potential recall bias in our survey study. A trend among dermatology residency applicants is pursuing a “gap year” dedicated to research or obtaining an additional degree to strengthen their chances of matching into this competitive specialty.1,2 This training interruption does not include the $11,324 that the average matched dermatology applicant spent on applications, away rotations, and interviews before the COVID-19 pandemic.3 We aimed to assess demographics, financial burden, and monetary support of dermatology residents who utilized a gap year. An online questionnaire was distributed, November 2019 to January 2020, to dermatology program directors to share with current residents at Accreditation Council for Graduate Medical Education approved programs in the United States and Canada. Data were analyzed in STATA. Institutional Review Board exempt-STUDY00007177 consent was waived. Of the 181 respondents (estimated 14.5% response rate of total dermatology residents enrolled in ACGME programs), 65.9% (n = 122) were female and 67.4% (n = 120) self- identified as White, while 20.02%, 2.2%, and 10.1% identified as Asian, Black, and other/multiple, respectively. A third (n = 60) reported undergoing a “gap year.” Single students were more likely to take a gap year than their married counterparts (P = .041). A logistic regression analysis, controlling for marital status, demonstrated that females had 2.32 times increased odds of taking a gap year (95% CI [1.15–4.87]; P = .02) compared to males (Table 1). Of gap-year takers, 71.7% reported some concurrent financial burden; 40.0% reported using personal savings to fund much of their research year; 13.3% utilized additional loans and 46.7% received a grant, or faculty/institutional stipend. Of grants/stipend recipients, the average monthly funding was $2,470.40 (Table 2). Table 1 - Demographics, loan burden, and USMLE scores of gap year versus nongap year respondents All respondents No gap year respondents Gap year respondents P-valuea Overall 188 121 (66.9%) 60 (33.1%) – Age, mean (SD) 30.0 (2.48) 30.2 (0.32) 29.9 (0.23) .466 Gender, n (%) Female 122 (65.9) 73 (60.3) 45 (76.3) .052 Male 63 (34.1) 48 (39.7) 14 (23.7) Marital status, n (%) Single 78 (42.6) 43 (36.1) 32 (53.3) .041 Married 105 (57.4) 76 (63.9) (46.7) Race, n (%)b Asian 36 (20.2) 21 (17.9) 15 (26.3) .323 Black 4 (2.2) 2 (1.7) 2 (3.5) Other/multiple 18 (10.1) 11 (9.4) 7 (12.3) White 120 (67.4) 83 (70.9) 33 (57.9) Self-identified family income as child, n (%) Low-income 7 (3.8) 6 (5.1) 1 (1.7) .418 Lower-middle class 60 (33.0) 38 (32.2) 22 (36.7) Upper-middle class 95 (52.2) 63 (53.4) 28 (46.7) Upper class 20 (11.0) 11 (9.3) 9 (15.0) Medical school loans, n (%) No 57 (31.3) 35 (28.9) 22 (36.7) .376 Yes 125 (68.7) 86 (71.1) 38 (63.3) Medical school loan burden ($), n (%) $0–49,999 7 (5.6) 5 (5.8) 2 (5.3) .865 $50,000–99,999 16 (12.8) 12 (14.0) 4 (10.5) $100,000–149,999 15 (12.0) 12 (14.0) 3 (7.9) $150,000–199,999 31 (24.8) 21 (24.4) 10 (26.3) $≥200,000 56 (44.8) 36 (41.9) 19 (50.0) Step 1 score, n (%) <240 37 (20.4) 21 (17.5) 16 (26.7) .215 ≥241 144 (79.6) 99 (82.5) 44 (73.3) USMLE, United States Medical Licensing Examination.aPearson χ2 tests for measure of association were used with significance set at P ≤ .05.bHispanic, Latino/a, or Spanish origin was also included as ethnicity option in the original survey, with 6 (54.6%) and 5 (45.5%) in the gap year and nongap year participants, respectively. Table 2 - Funding and financial burden for respondents taking a gap year Questions for respondents who reported taking a gap year Responses, n = 60 All sources of financial support for cost of living during gap year (not including direct research costs), one or more answers may be selected n (%) Personal savings or family 36 (60.0) Stipend by faculty/research mentor 8 (13.3) Outside grant personally obtained 6 (10.0) Institutional stipend 24 (40.0) Additional loans (federal or private) 11 (18.3) Other 2 (3.3) Majority source of financial support for cost of living during gap year (not including direct research costs) n (%) Personal savings or family 24 (40.0) Stipend by faculty/research mentor 1 (1.7) Outside grant personally obtained 6 (10.0) Institutional stipend 21 (35.0) Additional loans (federal or private) 8 (13.3) Approximate monthly amount ($) if majority support from stipend, grant, or institutional support, mean (SD) 2,470.4 (953.06) Would still have taken a research year without external funding?a n (%) Yes 15 (45.5) No 18 (54.6) If external funding was obtained, how well did this cover personal expenses? n (%) Completely covered expenses 19 (57.6) Covered more than half of expenses 10 (30.3) Covered less than half of expenses 3 (9.1) Did not at all cover my expenses 1 (3.0) Financial burden or hardship (if any) during research fellowship? n (%) No financial burden or hardship 17 (28.3) Minimal financial burden or hardship 12 (20.0) Some financial burden or hardship 16 (26.7) Moderate financial burden or hardship 7 (11.7) High financial burden or hardship 8 (13.3) aExternal funding is defined as funding from a stipend supported by a faculty/research mentor, grand personally obtained, or an institutional stipend. Nearly 80% of gap-year takers utilized personal savings, family support, and/or federal or private loans to help cover expenses, with a high rate of financial burden reported. For those who secured funding, the average amount was under 250% of the federal poverty level, raising the ethical question of whether gap years should be encouraged, even when they are “funded.” Without monetary support, over half of the respondents reported they would not have taken a gap year, emphasizing the importance of funding. Our study may suggest a potential gender bias as female residents were more likely than males to take a gap year when controlling for marital status in our study. However, this finding should be interpreted with caution given the small sample size. Other limitations to our study include a limited response rate and potential recall bias. While our study did not demonstrate significant associations between gap years and race, ethnicity or socioeconomic backgrounds, dermatology remains one of the least diverse medical specialties.2 Others have raised concern that the pressure for a gap year may introduce systematic bias into the applicant pool2 especially when no significant difference in match rates has been reported following gap years, except for potentially matching into top-ranked programs.4 In conclusion, gap years create a significant financial burden for medical students hoping to match into dermatology. This pressure to complete a gap year may self-select for more affluent applicants. More studies are needed to evaluate the true impact this has on diversity within the specialty. Conflicts of interest NG participated in clinical trials for Abbvie, Pfizer, Chemocentrix, and DeepX Health and has served on advisory boards and consulted for Novartis and Boehringer Ingelheim. The other authors have no conflicts of interest. Funding AJ was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences (grant UL1TR002494). GK was supported by the National Institutes of Health’s National Center for Advancing Translational Sciences (grant UL1TR002494). The content is solely the responsibility of the author and does not represent the views of any affiliated organizations. Study approval The authors confirm that any aspect of the work covered in this manuscript that has involved human patients has been conducted with the ethical approval of all relevant bodies. Author contributions AJ and GK: Participated in research design, performance of research, data analysis, and writing of the paper. RLF: Participated in data analysis and writing of the paper. KJR: Participated in writing of the paper. NG: Participated in research design and writing of the paper. Acknowledgments We gratefully acknowledge the contributions of Drs. Baho Sidiqi, MD, MPH, Erin F. Gillespie, MD, Chunyu Wang, MD, Melissa Dawson, MS, and Abraham J. Wu, MD, in their excellent article titled “Mind the Gap: An Analysis of ‘Gap Year’ Prevalence, Productivity, and Perspectives Among Radiation Oncology Residency Applicants,” which provided a framework for the survey tool utilized in our study, with authors’ permission.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.365
Threshold uncertainty score0.265

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.062
GPT teacher head0.394
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations3
Published2023
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