Defining ethnically and racially diverse populations in adult trauma and injury research in high-income countries: A scoping review
Bibliographic record
Abstract
This scoping review aims to examine how ethnically and racially diverse populations have been defined and identified in adult trauma and injury epidemiological research conducted in high-income countries, specifically Australia, New Zealand, the United States, the United Kingdom, and Canada. Despite evidence that trauma and injury outcomes vary across racial and ethnic groups, there is limited understanding of how these populations are identified and defined in epidemiological research. Inconsistent or unclear definitions of race and ethnicity can limit comparability between studies and hinder efforts to address health inequities. The review will systematically explore and summarise existing literature to identify the terminology, indicators, and classification systems used to describe ethnically and racially diverse groups in this field. Eligible studies will include adult populations identified as culturally and linguistically diverse (CALD), migrants, refugees, or racial/ethnic minorities (e.g. Black, Asian, Hispanic, Pacific Islander, POC, BIPOC) residing in the selected high-income countries. Studies focused exclusively on majority populations, Indigenous peoples, or paediatric cohorts will be excluded. Data will be charted to capture definitions, measurement methods, data sources, and reporting practices related to race and ethnicity in trauma and injury epidemiology. The review will provide a descriptive overview of how these populations are represented and classified, highlight inconsistencies and gaps in current reporting practices, and identify opportunities to improve conceptual clarity and methodological consistency. Findings will inform more inclusive and standardised approaches to identifying ethnically and racially diverse populations in future epidemiological research, ultimately supporting more equitable and transparent trauma and injury data collection and analysis across high-income countries.
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 | 0.032 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".