Remembering Freddie Gray: Medical Education for Social Justice
Bibliographic record
Abstract
Recent attention to racial disparities in law enforcement, highlighted by the death of Freddie Gray, raises questions about whether medical education adequately prepares physicians to care for persons particularly affected by societal inequities and injustice who present to clinics, hospitals, and emergency rooms. In this Perspective, the authors propose that medical school curricula should address such concerns through an explicit pedagogical orientation. The authors detail two specific approaches-antiracist pedagogy and the concept of structural competency-to construct a curriculum oriented toward appropriate care for patients who are victimized by extremely challenging social and economic disadvantages and who present with health concerns that arise from these disadvantages. In memory of Freddie Gray, the authors describe a curriculum, outlining specific strategies for engaging learners and naming specific resources that can be brought to bear on these strategies. The fundamental aim of such a curriculum is to help trainees and faculty understand how equitable access to skilled and respectful health care is often denied; how we and the institutions where we learn, teach, and work can be complicit in this reality; and how we can work toward eliminating the societal injustices that interfere with the delivery of appropriate health care.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.013 |
| 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".