Bibliographic record
Abstract
Background: Racism, the differential treatment of people based on their perceived racial or ethnic identity, causes health inequities between racial groups. An absence of race or ethnicity data (RED) in healthcare makes evaluation and awareness of health inequalities caused by systemic racism challenging. Current literature is scarce on collection methods of RED in healthcare globally. Methods: English language references and grey literature published in MEDLINE, Embase, and relevant sources between January 1, 2000, and July 3, 2021, were identified after consultation with a research librarian. Abstracts were evaluated for inclusion and exclusion criteria. Thematic analysis and data extraction were conducted after full-body reviews. Studies included in the final review focused on participants ≤18, were based in a healthcare facility and collected race or ethnicity data. Results: A total of 1,193 references were collected in the initial search (296 MEDLINE, 894 Embase, 3 other). After a full-body evaluation, 28 references were retained and included in the final analysis. Articles were set in the United States (n=7), Canada (n=5), Australia (n=4), and the United Kingdom (n=1). RED was collected using Electronic Medical Records (n=8), Electronic Health Records (n=6), collaborative studies (n=2), Patient Chart Documentation (n=1), and National Emergency Services Information System (NEMSIS) data (n=1). RED was collected in Tertiary care centers (n=8), Secondary care centers (n=1), a Primary care center (n=1), and a Quaternary care center (n=1). Racial and ethnic categories discussed in the literature included: White, Hispanic, Black, Indigenous, Aboriginal, and Asian. Six articles explicitly reported a need for more RED collection. Conclusion: Collecting RED is critical to understanding health inequities and the impacts of racism in healthcare. Globally, there is limited information on RED collection. We strongly endorse the recommendation of the BC Office of Human Rights Commissioner on the collection of RED in all age groups, including pediatrics. Capturing RED respectfully, meaningfully, and accurately across all groups will help identify potential associations, barriers, and inequities in health outcomes, helping to mitigate and eliminate systemic racism in healthcare.
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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 | 0.127 | 0.447 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".