Abstract: The Profile of the Accepted Applicant 2026 examines how respondents prepared for, financed, and navigated applications to health professions programs during the 2025–2026 cycle. Administered over the fall 2025 and spring 2026, the survey covers application costs, first-year educational expenses, respondent demographics, medical and veterinary admissions, situational judgment tests, and STEMM pathway programs. The findings describe the respondents to these surveys and should be interpreted alongside the methods, sample limitations and cohort definitions in the full report.

Understanding the applicant experience

Applying to a health professions program involves more than submitting an application. Applicants make choices about test preparation, school lists, financial resources, interview opportunities, and the communities where they hope to train. This report brings those experiences together so applicants, advisors, and educators can examine the practical barriers and sources of support described by survey respondents.

The report follows applicants across two survey periods. Fall responses capture experiences during the application cycle; spring responses describe the experiences of accepted applicants. The surveys were anonymous, and responses were not linked across periods. Differences between the fall 2025 and spring 2026 groups therefore describe different response groups rather than changes tracked in the same individuals.

Themes in the report

• Applicants draw upon their savings, gifts from family/friends, and personal credit before and during the application process. The financial chapters examine how those resources vary among respondents.
• The source report identifies differences in reported first-year costs and grants across medical, dental and veterinary respondents, and examines how applicants researched financing options.
• The report examines how application timing, academic metrics, and interview experiences affect admissions outcomes. These descriptive comparisons do not by themselves establish why an admissions decision occurred.
• Casper and PREview results are examined in relation to applicant characteristics, application timing, and respondents’ views of situational judgment tests.
• The pathway chapter combines current-cycle participation findings with explicitly identified historical analyses. Readers should check the cohort and year attached to each comparison.

Read the full Profile of the Accepted Applicant 2026

1. About the study: Background, recruitment, methods and disclosures for the Profile of the Accepted Applicant 2026.
2. Respondent demographics: Who responded to the Fall 2025 and Spring 2026 applicant experience surveys.
3. Application costs: Applicant savings, financial support, credit scores, and debt before applying.
4. Financing professional school: Reported first-year costs, grants, loans, and the financial barriers to professional education.
5. Differences in financial resources: How reported application costs and financial resources differ among respondent groups.
6. Medical school applications and interviews: MCAT preparation, application timing, interview screening and interview invitations.
7. Situational judgment tests: Casper and PREview findings, subgroup comparisons and supplemental figures.
8. Veterinary school applications: Veterinary application timing, interviews and communication with admissions teams.
9. STEMM pathway programs: Pathway participation and experiences, with historical analyses retained from the source.

Sample and interpretation

The surveys recruited volunteers through social media and SDN forums. They are not a representative sample of all applicants. Sample sizes differ across questions and subgroup analyses, and small groups require particular care. A reported association should not be treated as an admissions prediction or a causal explanation. Read About the study and Respondent demographics before comparing results across chapters.

The report presents the source tables and figures within the relevant chapters. The narrative and charts should be read together, with attention to the units, denominators, survey period, and profession shown. The historical pathway analyses retain their earlier cohort labels so they can be distinguished from the 2025–2026 findings.

Earlier report and related analysis

The 2025 report is available through SDN Review, including its downloadable PDF and citation record. Return to Research reports to browse the series.

The separate 2026 analysis of generative AI in prehealth applications is related reading. It is maintained as a separate article rather than a chapter of this report.

Conflict of interest disclosure

There are no conflicts of interest to report. The design and execution of this survey are fully supported by the Student Doctor Network, a non-profit 501(c)(3) educational organization based in the United States.

AI use

Comment summaries were performed using ChatGPT 5.6 (chat.openai.com) and included in this report with light editing. Claude 5 has been used to summarize survey results in this report. ChatGPT, Claude, and Gemini were used to summarize multiple sources. Grammarly has assisted in drafting and editing this report in Google Workspace. ChatGPT was also used to organize and format the report for online publication.

References

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2. PA Harris, R Taylor, BL Minor, V Elliott, M Fernandez, L O’Neal, L McLeod, G Delacqua, F Delacqua, J Kirby, SN Duda, REDCap Consortium, The REDCap consortium: Building an international community of software partners, J Biomed Inform. 2019 May 9 [doi: https://doi.org/10.1016/j.jbi.2019.103208]
3. Farkouh, K., Hogan, J., & Barton, M. (2025). A Prescription for Debt—How Federal Loan Caps Burden Medical Students. JAMA, 334(22), 1977–1978. https://doi.org/10.1001/jama.2025.19686
4. Professional Transition Strategies. (2022, October 24). How dental school debt compares to medical school debt. Archived at Internet Archive Wayback Machine, accessed September 26, 2026: https://web.archive.org/web/20251221042126/https://professionaltransition.com/how-dental-school-debt-compares-to-medical-school-debt/
5. Istrate EC, Kimner S, Booker CL, West KP. ADEA Trends in Dental Education 2024–25: A New Beginning for Oral Health Education. Washington, DC: American Dental Education Association; 2025. https://www.adea.org/docs/default-source/adea-main/publications/adea-trends/adea_trends_2024_25.pdf?sfvrsn=1384b8f6_3
6. American Student Dental Association. Dental Student Debt. https://www.asdanet.org/index/get-involved/advocate/issues-and-legislative-priorities/Dental-Student-Debt
7. Education Data Initiative. Average Medical School Debt. https://educationdata.org/average-medical-school-debt
8. CHG Healthcare. 2024 medical school debt survey shows growing burden for new physicians. https://chghealthcare.com/blog/medical-school-debt
9. American Veterinary Medical Association. (2023, May 25). Chart of the month: Good news on student debt. AVMA@Work Blog. https://www.avma.org/blog/chart-month-good-news-student-debt
10. Persichetti, J. (2025, March 4). How veterinary school graduates can start a practice with high student debt. dvm360. https://www.dvm360.com/view/how-today-s-veterinary-school-graduates-can-start-their-own-practices-even-with-high-student-debt
11. Wielk, E., & Stein, T. (2024, April 4). Measuring the return on investment of higher education: Breaking down the complexity. Bipartisan Policy Center. https://bipartisanpolicy.org/explainer/measuring-the-return-on-investment-of-higher-education-breaking-down-the-complexity/
12. Lenihan, D., & Mayrath, M. (2023, November 13). An effective methodology to boost the socio-economic diversity of U.S. med students and future doctors. Medical Economics. https://www.medicaleconomics.com/view/an-effective-methodology-to-boost-the-socio-economic-diversity-of-u-s-med-students-and-future-doctors
13. Scott, D. (2023, June 23). The American doctor deserts. Vox. https://www.vox.com/policy/23753724/physician-doctor-shortage-primary-care-medicare-medicaid-rural-health-care-access
14. Moran, M. (2023, July 17). Medical programs send budding doctors to rural county ‘healthcare deserts’. National Association of Counties. https://www.naco.org/articles/medical-programs-send-budding-doctors-rural-county-%E2%80%98healthcare-deserts%E2%80%99
15. American Hospital Association. (2025, June 13). Rural hospitals at risk: Cuts to Medicaid would further threaten access. Archived at Internet Archive Wayback Machine, accessed September 26, 2026: https://web.archive.org/web/20260214121534/https://www.aha.org/fact-sheets/2025-06-13-rural-hospitals-risk-cuts-medicaid-would-further-threaten-access
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17. Dore, K.L., Reiter, H.I., Kreuger, S. et al. CASPer, an online pre-interview screen for personal/professional characteristics: prediction of national licensure scores. Adv in Health Sci Educ 22, 327–336 (2017). https://doi.org/10.1007/s10459-016-9739-9
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19. Bardach, L., Rushby, J. V., & Klassen, R. M. (2021). The selection gap in teacher education: Adverse effects of ethnicity, gender, and socio-economic status on situational judgment test performance. The British journal of educational psychology, 91(3), 1015–1034. https://doi.org/10.1111/bjep.12405
20. Rosales, V., Conley, C., & Norris, M. C. (2024). Racial and Ethnic Disparities in Situational Judgment Testing Among Applicants to an Anesthesiology Residency Program. Journal of graduate medical education, 16(2), 140–145. https://doi.org/10.4300/JGME-D-23-00360.1
21. Johnson, E., & Komnenic, A. (2025, April 14). Shut out of medical school, he blames controversial admissions test which experts say lacks evidence. CBC News. Retrieved September 18, 2026, from https://www.cbc.ca/news/gopublic/casper-test-medical-school-1.7507308
22. MacIntosh A, Henning C, Altef S, et al. (2026) Validity of constructed-response situational judgement tests in health professions education: A systematic review and meta-analysis. Med Educ., 1-13. doi: https://doi.org/10.1111/medu.70245
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24. Royal, K.D., Carpentieri, A.M., Santos, R., Stenulson, D.W. (2026). PREview scores and applicant conduct indicators: distinguishing current professional readiness from historical events. Front. Med. 13:1884967. doi: https://doi.org/10.3389/fmed.2026.1884967
25. Catlett, J. L., II, Sarfo, E. K., Maysonet, J., Parkas, V., & Swartz, T. H. (2026). Validity and equity of the AAMC PREview situational judgment test in U.S. medical school admissions. Medical Teacher, 48(8), 1384–1392. doi: https://doi.org/10.1080/0142159X.2026.2621963
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27. Grbic, D., Andriole, D., Hunter, B., Poll-Hunter, N., & Young, G. (2026, August). Pathway programs’ participation among U.S. MD-granting medical school matriculants: A national study (Data Snapshot). Association of American Medical Colleges. https://doi.org/10.15766/datasnapshot_PATHWAY_PROGRMS_26

Abstract: The 2026 HPSA Applicant Experience Survey reveals how generative AI is used in prehealth applications for professional school admissions, including by applicants, admissions committees, and advisors. Findings show 60% of premed applicants distrust genAI search outputs, and accepted students use AI tools less frequently than peers. Prehealth candidates primarily utilize genAI for essay feedback, character limits, and interview preparation rather than ghostwriting. As application readers become more sensitive to changes in communication style, applicants should recognize that over-reliance on genAI can jeopardize their application success. Higher education graduate and professional programs should clarify appropriate, transparent usage of genAI tools and further study usage among applicants from resource-limited communities.

The rise of generative AI (genAI) has disrupted traditional teaching and learning. A 2025 survey shows that over 90% of students use AI tools in the classroom¹; however, their use in high-stakes professional school admissions remains fluid and mysterious. We examine results from the 2026 HPSA Applicant Experience Survey to understand how applicants feel about using generative AI in prehealth applications. We briefly compare our findings with the 2025 Student Voice survey (opens in new tab) run by Inside Higher Education.²

Classroom vs. admissions uses

The Student Voice survey found that about 80% of surveyed college students use generative AI tools in their classes. While most respondents use the tools to brainstorm ideas, support self-directed learning, or quiz themselves before critical exams, others use them to summarize lectures or required reading to “save time.” About 14% of respondents refused to use genAI in their courses. Students who used genAI admit that the tools have negatively impacted their critical thinking skills. These results are similar to those in the HPSA GenAI survey (Figure 5).³

The 2026 HPSA Applicant Experience Survey shows that applicants rely on genAI to prepare for the health professions admissions process.

• Generate ideas for questions/prompts, 29.5%
• Get feedback for drafts/revisions, 57.4%
• Learn about/research programs or schools, 34.6%
• Refining my narrative (changing tone), 22.4%
• Fitting for desired length (character or word limit), 40.5%
• Summarizing websites, videos, or webinars (why this program), 15.2%
• Determining whether detectors sense my writing was AI-generated, 11.0%
• Preparing for interviews, 45.6%
• Never used generative AI tools, 23.6%

Briefly, respondents’ social media use differs from that of other established internet sites.

• 86.1% used Google or typical search engines
• 60.8% used social media
• 40.5% used genAI searches
• 59.5% used directories like MSAR, VMSAR, and DSE
• 63.3% looked up individual program websites

Only 10% of respondents are paying to access improved genAI tools.

What about evaluators?

We have previously highlighted genAI guidance for applicants from central application services but not for references.³ Respondents believe that their evaluators and advisors use genAI tools. However, most are unaware that their evaluators and advisors use genAI to determine financial aid or scholarship packages.⁴

• Admissions committees use genAI tools to evaluate my application: 50% agree, 29.7% are neutral, 20.3% disagree.
• Professors use genAI to evaluate my assignments and essays: 31.1% agree, 40.5% are neutral, 28.4% disagree.
• Financial aid administrators use genAI to determine my financial aid package: 20.5% agree, 42.5% are neutral, 31.9% disagree.

Resisting the pressure to “succeed”

Among our accepted student respondents, we surveyed their attitudes toward how they expect genAI to be incorporated into their education. Although more applicants use genAI tools, 60% of respondents do not trust the information they get from genAI searches and tools. Rather, 59% appreciate how genAI tools help them complete tasks more quickly.

The Student Voice survey confirms that students often use genAI tools in ways that violate academic integrity codes because of pressure to get good grades. Younger students who use genAI tools seem to value academic integrity policies less, while older students rely on genAI tools when pressed for time. Most appreciate clear policies that specify the appropriate use of genAI rather than policies that focus on policing and punishing improper use.

The HPSA Applicant Experience survey asked respondents to compare their use of genAI tools with that of their peers. When we compared our applicant pool (Fall 2025) with our accepted pool (Spring 2026), more accepted respondents reported using genAI tools less than their peers; a small increase was also observed among those who reported using genAI tools more than their peers. The proportion of respondents who forswear using genAI tools appeared slightly lower in the accepted than in the applicant pool.

Interestingly, most Veterinary Medicine respondents reported not using genAI tools or using them less often than their peers.

Our data suggest that applicants in our survey are less likely to use genAI tools, and that admissions decisions favor those with relatively less expertise in genAI compared to their peers. We were curious how accepted students see genAI being integrated into their professional education. Our responses show mixed feelings.

• GenAI gives applicants a more even playing field: 26.4% agree, 15.3% neutral, 58.3% disagree.
• Applicants feel they must use genAI tools to get accepted: 2.7% somewhat agree, 16.4% neutral, 19.2% somewhat disagree, 61.6% strongly disagree.
• I use genAI tools since no one is helping me: 9.9% strongly agree, 12.7% somewhat agree, 15.5% neutral, 14.1% somewhat disagree, 47.9% strongly disagree.
• GenAI gives me very useful feedback about the competitiveness of my application: 8.5% strongly agree, 31.0% somewhat agree, 18.3% neutral, 12.7% somewhat disagree, 29.6% strongly disagree.
• I expect genAI will be integrated into the curriculum of my professional program: 15.3% strongly agree, 20.8% somewhat agree, 27.8% neutral, 13.9% somewhat disagree, 22.2% strongly disagree.

These insights may prompt discussion with applicants and new matriculants about integrating genAI as a partner that improves critical thinking and diagnostic skills, even as the technology rapidly improves. Educators should consider their students’ reticence to use genAI without clear guidance and examples on appropriate use.

Reflection

As genAI technologies become more mainstream, most health professional student applicants remain skeptical until they understand how to use them properly. Respondents expect their prehealth and health professional institutions to use genAI when assessing application profiles, and they seek validation for their own choices to use genAI tools sparingly. These results suggest that institutions can craft messages and evidence showing that using genAI does not unfairly advantage applicants who rely heavily on these tools.

After several application cycles, admissions committee members can better discern how consistent an applicant’s writing style is with other components of their application. While applicants may use genAI tools to meet self-imposed deadlines to “submit early,” drastic changes in writing style among essays or resume/activity descriptions may raise concerns. Applicants generally underestimate how detectable this difference is between genAI-written responses and answers from live interview sessions.

A recent Inspira Advantage study suggests that premed applicants may also turn to genAI for affirmation during the application process.⁵ With one in eight adolescents reportedly using genAI tools to address stress or their mental health,⁶ this finding may indicate how much we should expect future health professional students to use genAI to navigate challenges during their education. A study of 50,000 university students suggests a relationship between high usage of genAI tools to test anxiety that can lead to burnout.⁷ A Gallup survey also suggests that respondents from high-income nations who use social media heavily self-report higher stress and a more pessimistic view of life.⁸ We plan to survey 2026-2027 applicants to further gauge a relationship between their experience of pressure during the application process and their use of genAI.

Interestingly, our findings seem to contradict other studies suggesting that lower-income college or university applicants are more likely to use genAI tools.⁹ Because college/university applicants who rely on genAI-written essays are rejected at higher rates, lower-income applicants are also less likely to be admitted.¹⁰

We hope that genAI tools will continue to improve so that students and applicants can gain more confidence. Until then, applicants will weigh the benefits and risks to their chances of acceptance and subsequent learning.

Discussion questions

1. Authenticity vs. AI editing

The HPSA survey indicates that accepted applicants use generative AI less often than their peers, and evaluators notice discrepancies when written submissions do not align with live interview responses.

• Where should applicants draw the line between using AI for low-stakes structural feedback (e.g., character limits or proofreading) and relying on it so much that it dilutes their authentic voice?
• How can candidates ensure that AI refinement doesn’t erase key details of their personal narrative?

2. Equity and the “level playing field”

Most surveyed applicants (58.3%) disagree that generative AI creates a more even playing field.

• Given that under-resourced applicants may lack access to professional advisors or paid editing services, can free AI tools help bridge the advising gap, or do they risk penalizing these applicants through overreliance?
• What responsibilities do admissions committees have to ensure that candidates without private application coaching are not disadvantaged?

3. Institutional transparency and guidance

Half of respondents believe admissions committees actively use AI to evaluate applications, yet explicit policies on applicant AI usage remain rare or punitive.

• What specific guidelines should health professions programs publish to set clear, constructive boundaries for generative AI use?
• Should admissions committees also disclose if and how they use AI tools to screen or evaluate incoming application files?

4. AI as a support tool for stress and mental health

The article highlights that prehealth applicants increasingly use AI as a coping mechanism for application stress, while broader studies link heavy AI use to student burnout and anxiety.

• What drives candidates to turn to AI chatbots for affirmation and guidance during the application process?
• How can prehealth advising programs address the root causes of application anxiety so candidates do not rely on AI tools for emotional support?

5. Disciplinary variations in AI adoption

Veterinary medicine applicants demonstrated significantly higher resistance to generative AI than premed candidates, with over half reporting they never use AI tools.

• What professional, cultural, or institutional factors might explain why applicants in different health fields approach AI tools so differently?

Takeaways

1. High-stakes admissions vs. everyday classroom learning

• Classroom setting (Student Voice & Copyleaks surveys): Over 80%–90% of students report using generative AI for coursework to brainstorm, summarize, or save time.
• Admissions setting (HPSA survey): Overall usage is lower, with 23.6% of prehealth applicants reporting they never use genAI. High-stakes admissions make candidates cautious because they fear penalties, lack clear institutional policies, and worry that over-relying on genAI will alter their writing style or authentic voice, which admissions readers can detect.

2. General applicants vs. accepted applicants

• Lower AI usage among accepted students: Accepted applicants reported using genAI less frequently than their peers (53.2% vs. 37.9% of total applicants).
• Distrust and skepticism: Admissions committees appear to favor candidates with less reliance on genAI or greater authenticity. How can this preference be measured?

3. Differences across professional fields (premed vs. veterinary medicine)

Field-specific adoption: Veterinary medicine applicants showed much higher resistance to genAI, with over half (53.1%) reporting they never use genAI tools compared to 24.2% of general health applicants.

4. Perceived risks vs. utility (low-stakes vs. high-stakes tasks)

Task delegation: Applicants predominantly use genAI for lower-stakes assistance (e.g., proofreading drafts at 57.4%, preparing for interviews at 45.6%, and fitting word counts at 40.5%). They avoid using it to generate core content due to a strong consensus (58.3%) that genAI does not create a “level playing field” and is not required to gain admission (61.6% strongly disagree).

5. Income disparities and application outcomes

Socioeconomic divergence: While studies like Chirikov et al. suggest lower-income applicants are more likely to turn to genAI tools (often as a substitute for costly private coaching), studies like Alonso et al. show that applicants who rely heavily on AI-generated essays face higher rejection rates, inadvertently resulting in lower acceptance rates for those candidates.

Peer review acknowledgment

A preliminary draft of this article was voluntarily reviewed by Arush Chandna of Inspira Advantage. He did not receive financial compensation as a reviewer, but we offered him an opportunity to document his participation in ORCID. He previously published an article in SDN Review about Inspira Advantage’s surveys without compensation.

Conflict of interest disclosure

The author of this article is employed as the editor-in-chief of SDN Review and the Director of Advising Services for Student Doctor Network. He is the primary investigator of the Applicant Experience Survey, which has been approved as an IRB-exempt study (TIER IRB protocol 424091202).

AI use

Generative AI tools (Grammarly) were used to edit and proofread original drafts of this article, develop an abstract, and create the list of takeaways and discussion questions. ChatGPT was used to format this article according to site style guidelines.

References

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2. Flaherty, C. (2025, August 29). Survey: College Students’ Views on AI, Inside Higher Ed. https://www.insidehighered.com/news/students/academics/2025/08/29/survey-college-students-views-ai (opens in new tab) . Accessed July 6, 2026.
3. Chuck, E. (2024, September 17). How are you using AI? Generative AI survey results. Student Doctor Network Review, 18(9), Article 3. https://www.studentdoctor.net/2024/09/12/how-are-you-using-ai/
4. National Association of Student Financial Aid Administrators. (2026, April). Use of artificial intelligence in financial aid offices: Findings from a survey of financial aid professionals. https://www.nasfaa.org/uploads/documents/Use_of_AI_in_FAOs_Survey_Report.pdf (opens in new tab). Accessed July 7, 2026.
5. Chandna, A. (2026, September 8). 55% of pre-med students believe AI can improve their mental health during their applications and careers (M. Kirsch, Ed.). Inspira Advantage. https://www.inspiraadvantage.com/blog/pre-med-students-believe-ai-can-improve-their-mental-health (opens in new tab)
6. Siliezar, J. (2025, November 18). One in eight adolescents and young adults use AI chatbots for mental health advice. Brown University School of Public Health. https://sph.brown.edu/news/2025-11-18/teens-ai-chatbots (opens in new tab)
7. Kapustynska, V. (2026). Generative AI Use and Burnout Risk in Higher Education: A Cross-Sectional Analysis of 50,000 Students. RESEARCH REVIEW International Journal of Multidisciplinary, 11(7), 84-98. https://doi.org/10.31305/rrijm.2026.v11.n07.009 (opens in new tab)
8. Ray, J., and Ron-Levey, I. (2026, October 10). Social media and wellbeing: It’s complicated. Gallup. https://news.gallup.com/poll/715433/social-media-wellbeing-complicated.aspx (opens in new tab)
9. Chirikov, I., Smirnov, I., and Kizilcec, R. F. (2026). Generative AI use and misuse call for assessment reform in higher education. Science, 392(6800), 818–820. https://doi.org/10.1126/science.aec5115 (opens in new tab)
10. Alonso, J. (2026, May 8). Low-income students more likely to submit AI-generated admissions essays. Inside Higher Ed. https://www.insidehighered.com/news/admissions/traditional-age/2026/05/08/study-explores-ai-written-admissions-essays (opens in new tab). Accessed July 7, 2026.

Abstract: A medical student reflects on taking a medical leave of absence after a serious postoperative complication and on the isolation and guilt that accompanied recovery. The experience reshaped the author’s view of medical training and inspired Pause With a Purpose, an organization being developed to support students and professionals facing illness, caregiving responsibilities, burnout, and other life-altering circumstances. The essay calls for a culture that treats personal health as a foundation for a sustainable medical career.

As medical students, we are often confined to a repetitive cycle that defines much of our four years: studying, eating, barely sleeping, and then studying even more. We learn early to push through exhaustion and to treat our own well-being as something that can wait until after the next exam, the next board score, the next milestone. Somewhere along the way, many of us begin to believe that slowing down is the same thing as falling behind. I believed that too, until my own health forced me to learn otherwise.

Before matriculating into medical school, I was diagnosed with a chronic condition that would ultimately require multiple surgeries over several years. Rather than prioritizing treatment, I did what so many students do: I postponed. There was always another prerequisite, another interview, another reason why my health could wait. Once medical school began, I continued pushing through, attending lectures, preparing for board examinations, and maintaining the appearance of functioning normally while quietly managing pain, recurrent complications, and fatigue behind the scenes.

I convinced myself this was what commitment looked like. I feared that acknowledging my limitations would mean jeopardizing the future I had worked so hard to build.

Eventually, my body made the decision for me. Shortly after undergoing another procedure timed carefully around my board examinations, I developed a serious postoperative complication that landed me in the intensive care unit. In a single day, I went from pre-studying for my upcoming rotations to fighting for far more than my place in a class rank. Waking up in that ICU bed, surrounded by my family and the team caring for me, I was forced to confront a truth I had spent nearly a decade avoiding: I could no longer put medicine ahead of my own health.

For the first time in my journey toward becoming a physician, I took a medical leave of absence.

The physical recovery was long, but the emotional recovery was in many ways harder. While I healed at home, I watched classmates progress through rotations and celebrate milestones I had expected to share alongside them. Like many students who step away, I struggled with isolation, guilt, and the quiet fear that I had somehow failed. After years of sacrifice to earn my seat in medical school, feeling left behind was devastating in a way I had never anticipated.

But those months of recovery gave me something medical school never had: time to reflect. Somewhere in that stillness, I came to a realization that changed how I view this profession. Medical school is only a small chapter in what I hope will be a long career. My path looked different from the paths of those around me, but different does not mean failure. If I was going to fight my way through this chapter, I wanted my experience to serve a greater purpose than my own recovery.

That conviction became the foundation for Pause With a Purpose, an organization I am now building to support students and professionals across medicine who are navigating serious illness, mental health challenges, caregiving responsibilities, burnout, or other life-altering circumstances during their training and careers. The mission is simple: to create the kind of support system I wish I had during the most difficult moments of my own journey, and to help erase the stigma that surrounds stepping away.

Even in these early stages, the need has been impossible to ignore. As I began speaking openly about my experience, I was surprised by how many people quietly shared stories remarkably similar to my own. I heard from students who took leaves to address depression and burnout, trainees recovering from cancer treatment and major surgery, and colleagues who stepped away to care for family members or navigate personal crises. The circumstances varied, but the underlying fear was almost always the same: the belief that taking time away from training would permanently define them.

Often, these individuals did not need solutions. They simply needed someone willing to listen and to remind them that they were not alone. That is the community Pause With a Purpose exists to build.

Medicine’s culture of self-sacrifice runs deep. From the earliest stages of training, we are praised for perseverance and endurance, and those traits have real value, but they become dangerous when they teach future physicians that asking for help is weakness, or that personal well-being is secondary to professional achievement. We would never advise our patients to ignore serious illness in order to remain productive, yet many of us hold ourselves to precisely that standard.

My leave of absence taught me what I wish I had understood much earlier: taking a pause is not failure. In fact, stepping away may be one of the most responsible decisions a trainee can make when facing significant health challenges. Becoming a physician is not a race, and the goal of training should never be to identify who can endure the most suffering. It should be to develop healthy, compassionate, resilient physicians capable of caring for others over the course of a lifetime.

Today, I have returned to clinical rotations stronger than I have been in years, and I am preparing for my Level 2 board examinations while continuing to develop Pause With a Purpose alongside my training. I never imagined that my journey into medicine would also make me an advocate for the people within it. Yet after everything this chapter has taught me, I know that this work will remain part of my career long after my training ends.

To any student quietly struggling behind the scenes, your health is not a detour from your future in medicine. It is the foundation of it. Taking a pause did not derail my journey toward becoming a physician. It helped save it, and it gave that journey a purpose larger than myself.

Conflict of interest disclosure

The author is the founder of Pause With a Purpose, an organization discussed in this article.

AI use

AI assistance was used to draft the abstract and conflict of interest disclosure and to format the article for publication.

Photo by Oluwaseyi Akinlolu on Unsplash.