Navigating Expectations in the Transition to the Pass/Fail Step 1 Exam: Tensions and Recommendations From Student Leaders of the NEXT Step 1 Project
Bibliographic record
Abstract
ABSTRACT: The transition of the United States Medical Licensing Examination (USMLE) Step 1 from 3-digit to pass/fail scoring set the stage for one of the most significant changes in medical education in several decades. Although originally designed for a binary competency decision around licensure, the Step 1 exam score was famously used as a major criterion for residency interview selection. The transition to pass/fail grading sought to address the issue of students focusing on Step 1 exam preparation at the expense of their formal medical school curriculum and their well-being. However, trainees, advisers, faculty, and residency program directors quickly identified several unintended consequences of this decision. In response, a collaborative grassroots effort was formed among multiple stakeholders, including an extensive network of trainees. The Navigating Expectations in the Transition to Pass/Fail Step 1 (NEXT Step 1) project aims to study this scoring change and develop consensus recommendations for all shareholders affected. In this study, student leaders of the NEXT Step 1 project use their lived experiences as trainees and members of this collaborative project to identify key tensions that arose due to the scoring change. Tensions were described within 3 domains: curriculum, student advising, and residency applications. The authors discuss each tension and provide recommendations for medical schools, faculty, residency programs, and students to consider moving forward in the era of the pass/fail Step 1 exam. Although most students involved in the project think the transition to pass/fail scoring on the Step 1 exam has been positive, there are many downstream consequences that need to be addressed to improve student well-being and fairness in the residency application process. The authors provide student-centered recommendations for these challenges and aim to provide an example of meaningful trainee engagement in academic medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".