Bias in Observed Assessments in Medical Education: A Scoping Review
Bibliographic record
Abstract
PURPOSE: Observed assessments are integral to medical education but may be biased against structurally marginalized communities. Current understanding of assessment bias is limited because studies have focused on single specialties, levels of training, or social identity characteristics (SIDCs). This scoping review maps studies investigating bias in observed assessments in medical education arising from trainees' observable SIDCs at different medical training levels, with consideration of medical specialties, assessment environments, and assessment tools. METHOD: MEDLINE, Embase, ERIC, PsycINFO, Scopus, Web of Science Core Collection, and Cochrane Library were searched for articles published between January 1, 2008, and March 15, 2023, on assessment bias related to 6 observable SIDCs: gender (binary), gender nonconformance, race and ethnicity, religious expression, visible disability, and age. Two authors reviewed the articles, with conflicts resolved by consensus or a third reviewer. Results were interpreted through group review and informed by consultation with experts and stakeholders. RESULTS: Sixty-six of 2,920 articles (2.3%) were included. These studies most frequently investigated graduate medical education [44 (66.7%)], used quantitative methods [52 (78.8%)], and explored gender bias [63 (95.5%)]. No studies investigated gender nonconformance, religious expression, or visible disability. One evaluated intersectionality. SIDCs were described inconsistently. General surgery [16 (24.2%)] and internal medicine [12 (18.2%)] were the most studied specialties. Simulated environments [37 (56.0%)] were studied more frequently than clinical environments [29 (43.9%)]. Bias favoring men was found more in assessments of intraoperative autonomy [5 of 9 (55.6%)], whereas clinical examination bias often favored women [15 of 19 (78.9%)]. When race and ethnicity bias was identified, it consistently favored White students. CONCLUSIONS: This review mapped studies of gender, race, and ethnicity bias in the medical education assessment literature, finding limited studies on other SIDCs and intersectionality. These findings will guide future research by highlighting the importance of consistent terminology, unexplored SIDCs, and intersectionality.
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 | 0.184 | 0.540 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.036 | 0.038 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.003 |
| 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".