Strategies to Promote Racial Healthcare Equity in Pain Medicine: A Call to Action
Bibliographic record
Abstract
In the past several years, many national events have illuminated the inequities faced by the Black community in all aspects of life, including healthcare. To close the gap in healthcare equity, it is imperative that clinicians examine their practices for disparities in the treatment of minority patients and for racial injustice and take responsibility for improving any issues. As leaders in pain medicine, we can start by improving our understanding of healthcare disparities and inequities among racial and ethnic minorities and translating that knowledge into a cultural transformation to improve the care of those impacted. In this paper, we identify the areas of medicine in which pain assessment and treatment are not equitably delivered. As we acknowledge these disparities, we will highlight reasons for these incongruences in care and clarify how clinicians can act to ensure that all patients are treated equitably, with equal levels of compassion.
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 | 0.099 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.027 |
| Scholarly communication | 0.019 | 0.032 |
| Open science | 0.006 | 0.033 |
| Research integrity | 0.028 | 0.049 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".