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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.000 |
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.
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 teacher head, 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".