A systematic review of racial health disparities among children and youth with physical disabilities
Bibliographic record
Abstract
PURPOSE: Children and youth who belong to a racially minoritized group commonly experience multiple and complex forms of discrimination and health disparities. The purpose of this review was to explore racial disparities in health care and health outcomes among children and youth with physical disabilities. METHODS: Six international databases (Ovid Medline, Healthstar, Embase, PsycINFO, Scopus, and Web of Science) were searched and screened for inclusion. A narrative synthesis was used to identify the common trends. RESULTS: Thirty-seven articles met the inclusion criteria, which involved 218 555 children and youth with various types of physical disabilities spanning over 29 years. We noted the following trends: (1) racial disparities in accessing or receiving care; (2) racial disparities in health outcomes and mortality rates; and (3) factors affecting racial disparities. Most studies reported at least one finding indicating that racially minoritized youth had differential access to care and/or disparities in health outcomes compared to white youth. CONCLUSIONS: Our findings highlight the concerning racial disparities among children and youth with physical disabilities within health care. There is an urgent need for advocacy and interventions at multiple levels to address the perpetual racism and racial disparities that racially minoritized youth with physical disabilities experience.Implications for rehabilitationThere is an urgent need for health care leaders and health care providers to address the systemic health inequalities in rehabilitation for racially minoritized children and youth with physical disabilities.Health care leaders and clinicians should recognize the racial disparities that racially minoritized youth with physical disabilities encounter in accessing or receiving care in addition to health outcomes.Health care leaders and decision-makers should advocate for policy change to optimize equitable and inclusive health care to enhance the well-being of racially minoritized children with disabilities.Health care providers should engage in training to understand how to recognize and address how intersectional forms of a child's identity such as disability, race, and socio-economic status can influence health care experiences and health outcomes
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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 | 0.010 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".