Exploring racial disparities and inequalities among children and youth with acquired brain injury: a systematic review
Bibliographic record
Abstract
PURPOSE: Racial minoritized children and youth with acquired brain injury (ABI) often experience multiple forms of discrimination. The purpose of this systematic review was to understand the racial disparities in health care among children and youth with ABI and their caregivers. METHOD: Six international databases (Ovid Medline, Embase, Healthstar, Psychinfo, Scopus, and Web of Science) were systematically searched for peer-reviewed articles. Studies were screened by two researchers who also conducted the data extraction and quality appraisal. A narrative synthesis approach was used to analyze the data. RESULTS: Of the 8081 studies identified in the search, 34 met the inclusion criteria, which involved 838,052 children and youth with brain injuries (or caregivers representing them) across two countries. The following themes were noted in the studies in our review: (1) racial disparities in accessing care (i.e., diagnosis, hospital admission, length of stay, rehabilitation treatment); (2) racial disparities in ABI-related health outcomes (i.e., functional outcomes and mortality rates); and (3) factors affecting racial disparities (i.e., sources in injury, insurance and expenditures, and intersectionality). CONCLUSIONS: Our findings reveal the concerning racial disparities among children and youth with ABI. Further research should explore solutions for addressing such racial disparities and solutions to address them.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".