Remembering Freddie Gray: Medical Education for Social Justice
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it