Impact of USMLE Step-1 accommodation denial on US medical schools: A national survey
Bibliographic record
Abstract
INTRODUCTION: In 2019, 4.6% of US-MD students self-identified as students with disabilities (SWD); many of these students will require accommodations on the USMLE Step-1 examination. Given the high-stakes nature of Step-1 for medical school advancement and residency match, SWD denied accommodations on Step-1 face considerable consequences. To date no study has investigated the rate of accommodation denial and its impact on medical school operations. METHODS: To investigate the rate of accommodation denial and evaluate whether Step-1 accommodation denial impacts medical school operations, a 10-question survey was sent to Student Affairs Deans and disability resource professionals at all fully-accredited US-MD granting programs. Two open-ended questions were analyzed using qualitative content analysis. RESULTS: Seventy-three of the 141 schools responded (52%). In the 2018-2019 academic year, 276 students from 73 schools applied for Step-1 accommodations. Of these, 144 (52%) were denied. Of those denied, 74/144 (51%) were delayed entry into the next phase of curriculum and 110/144 (76%) took the Step-1 exam unaccommodated. Of the 110 who took Step-1 without accommodations, 35/110 (32%) failed the exam, and 4/110 (3%) withdrew or were dismissed following exam failure. Schools reported varied investments of time and financial support for students denied accommodations, with most schools investing less than 20 hours (67%) and less than $1,000.00 (69%). Open-responses revealed details regarding the impact of denial on schools and students including frustration with process; financial and human resources allocation; delay in student progression; lack of resourcing and expertise; and emotional and financial burdens on students. DISCUSSION: Step-1 accommodation denial has non-trivial financial, operational, and career impacts on medical schools and students alike. The cause of accommodation denial in this population requires further exploration.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".