The Importance of Developing and Implementing an Inclusive Language and Image Policy in Medical Schools
Bibliographic record
Abstract
To the Editor: Exclusionary language and images in academic medicine are common 1 and harmful. 2,3 Sexist jokes are frequent during medical school lectures. 2 Of over 4,000 images in medical school lectures, 61% depicted men and 78% portrayed White patients. 1 Stigmatizing language also occurs; it worsens self-management of disease and increases bias. 3,4 A culture of inclusion requires shifting the language and images used in academic medicine. Inclusive language is “free of bias and avoids perpetuating prejudicial beliefs,” 5 and inclusive images represent patients’ diversity. We developed the Inclusive Language and Images Policy 6 (ILIP) to facilitate changing culture at our institution, using relevant literature reviews and stakeholders’ views. The ILIP applies to lectures, email signatures, and institutional documents. The components of ILIP are: Introduction to inclusive language and imagery, Statement of leadership support, Explanation of principles of inclusive language and imagery with resources and examples, 1 Guidance for email signatures, and Inclusive and diversity disclosure statement for presentations. The benefits of inclusive language and imagery are supported by evidence. 4,7 Inclusive language often uses person-first and strengths-based wordings rather than using a medical illness as a noun (e.g., “person with diabetes” rather than “a diabetic”). Inclusive language is nonjudgmental, facts-based, mentions personal characteristics only when medically relevant, nonstigmatizing, and uses the singular “they.” Many national societies provide population-specific guidelines. The American Psychology Association’s style guide discussions of bias-free language can guide presentations and writing. However, individual preferences of appropriate language should take precedence over established principles in one-on-one interactions. The ILIP is dynamic and should be adapted when necessary—for example, to acknowledge the history of the institution: its policies, celebrated figures, land, and achievements. We have added an Indigenous land acknowledgment to presentations as a local adaptation to support guidelines concerning truth and reconciliation. Uses of the ILIP should be monitored for adherence; one option is to include questions about inclusion on presenter evaluations. Continuing education may be needed to support faculty as they implement ILIP policies. Shifting the culture of medicine requires role modeling by faculty, and the ILIP can provide this framework.
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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 | 0.041 | 0.234 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.038 | 0.039 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".