Naming racism as a root cause of inequities in palliative care research: a scoping review
Bibliographic record
Abstract
BACKGROUND: Racial and ethnic inequities in palliative care are well-established. The way researchers design and interpret studies investigating race- and ethnicity-based disparities has future implications on the interventions aimed to reduce these inequities. If racism is not discussed when contextualizing findings, it is less likely to be addressed and inequities will persist. OBJECTIVE: To summarize the characteristics of 12 years of academic literature that investigates race- or ethnicity-based disparities in palliative care access, outcomes and experiences, and determine the extent to which racism is discussed when interpreting findings. METHODS: Following Arksey & O'Malley's methodology for scoping reviews, we searched bibliographic databases for primary, peer reviewed studies globally, in all languages, that collected race or ethnicity variables in a palliative care context (January 1, 2011 to October 17, 2023). We recorded study characteristics and categorized citations based on their research focus-whether race or ethnicity were examined as a major focus (analyzed as a primary independent variable or population of interest) or minor focus (analyzed as a secondary variable) of the research purpose, and the interpretation of findings-whether authors directly or indirectly discussed racism when contextualizing the study results. RESULTS: We identified 3000 citations and included 181 in our review. Of these, most were from the United States (88.95%) and examined race or ethnicity as a major focus (71.27%). When interpreting findings, authors directly named racism in 7.18% of publications. They were more likely to use words closely associated with racism (20.44%) or describe systemic or individual factors (41.44%). Racism was directly named in 33.33% of articles published since 2021 versus 3.92% in the 10 years prior, suggesting it is becoming more common. CONCLUSION: While the focus on race and ethnicity in palliative care research is increasing, there is room for improvement when acknowledging systemic factors - including racism - during data analysis. Researchers must be purposeful when investigating race and ethnicity, and identify how racism shapes palliative care access, outcomes and experiences of racially and ethnically minoritized patients.
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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 | 0.050 | 0.207 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.041 | 0.037 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".