Addressing disparities among children with cerebral palsy: Optimizing enablement, functioning, and participation
Bibliographic record
Abstract
PURPOSE: Recognizing health disparities among children with cerebral palsy (CP) is necessary for understanding potential risk factors for CP and for implementing early and effective preventative and intervention treatments. However, there is currently little and conflicting evidence regarding the direct impact of contextual factors such as socioeconomic status (SES) for children with CP in the United States. These contextual factors include the complex social determinants of health on prematurity, comprehensive informed obstetric management for minority and vulnerable populations, and cumulative adversity disproportionately experienced by children, by gender, minority status, immigration, poverty, and structural racism. METHODS: This study presents results from a review of health disparities among children with CP, using registry and population surveillance data from Australia, Canada, Scandinavia, the United Kingdom, Ireland, Turkey, and the United States. RESULTS: The review confirmed that there are significant health disparities among children with CP, both in terms of prevalence and severity, based on factors such as SES, neighborhood disadvantage, maternal education, gender, and minority status. CONCLUSION: Strategies need to be implemented in the United States to promote enablement and functioning among children with CP who face additional health disparities. This requires a greater understanding of population groups at increased risk, comprehensive assessment and care for young children with motor delays, and systematic population counts of children and adults with CP using registries and systems of neurodevelopmental surveillance across health, education, and community rehabilitation. These efforts also require sensitivity to structural and persistent racism, stigma, trauma-informed care, and culturally sensitive community engagement. Additional efforts are also required to improve outcomes over the life course for individuals living a life with CP from a framework of enablement, self-direction, equity and social justice.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".