Encouraging workforce diversity- supporting medical students with mobility and sensory disabilities
Bibliographic record
Abstract
PURPOSE: This article is prepared by the Association of Professors of Gynecology and Obstetrics Undergraduate Medical Education Committee and provides educators recommendations for optimizing inclusive education for our students with disabilities. Medical educators are increasingly encountering students with disabilities and have the responsibility of ensuring requirements are met. METHOD: Medical education committee members from the US and Canada reviewed the literature on disabilities in medical student education to identify best practices and key discussion points. An iterative review process was used to determine the contents of an informative paper. RESULTS: Medical schools are required to develop technical standards for admission, retention, and graduation of their students to practice medicine safely and effectively with reasonable accommodation. A review of the literature and obstetrics and gynecology expert opinion formed a practical list of accommodation strategies and administrative steps to assist educators and students. CONCLUSION: Medical schools must support the inclusion of students with disabilities. We recommend a collaborative approach to the interactive process of determining reasonable and effective accommodations that includes the students, a disability resource professional and faculty as needed. Recruiting and supporting medical students with a disability strengthens the diversity commitment and creates a more inclusive workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".