Structural Barriers to Student Disability Disclosure in US-Allopathic Medical Schools
Bibliographic record
Abstract
INTRODUCTION: Leaders in medical education have expressed a commitment to increase medical student diversity, including those with disabilities. Despite this commitment there exists a large gap in the number of medical students self-reporting disability in anonymous demographic surveys and those willing to disclose and request accommodations at a school level. Structural elements for disclosing and requesting disability accommodations have been identified as a main barrier for students with disabilities in medical education, yet school-level practices for student disclosure at US-MD programs have not been studied. METHODS: In August 2020, a survey seeking to ascertain institutional disability disclosure structure was sent to student affairs deans at LCME fully accredited medical schools. Survey responses were coded according to their alignment with considerations from the AAMC report on disability and analyzed for any associations with the AAMC Organizational Characteristics Database and class size. RESULTS: Disability disclosure structures were collected for 98 of 141 eligible schools (70% response rate). Structures for disability disclosure varied among the 98 respondent schools. Sixty-four (65%) programs maintained a disability disclosure structure in alignment with AAMC considerations; 34 (35%) did not. No statistically significant relationships were identified between disability disclosure structures and AAMC organizational characteristics or class size. DISCUSSION: Thirty-five percent of LCME fully accredited MD program respondents continue to employ structures of disability disclosure that do not align with the considerations offered in the AAMC report. This structural non-alignment has been identified as a major barrier for medical students to accessing accommodations and may disincentivize disability disclosure. Meeting the stated calls for diversity will require schools to consider structural barriers that marginalize students with disabilities and make appropriate adjustments to their services to improve access.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".