Perceived Discrimination and Increased Odds of Unmet Medical Needs Among US Children
Bibliographic record
Abstract
Our study examines the association between perceived discrimination due to race and unmet medical needs among a nationally representative sample of children in the United States. We used data from the 2016-2017 National Survey of Children's Health, a population-based cross-sectional survey of randomly selected parents or guardians in the United States. We compared results from the coarsened exact matching (CEM) method and survey-weighted logistic regression to assess the robustness of the results. Using self-reported measures from caregivers, we find that ∼2.7% of US children have experienced racial discrimination with prevalence varying significantly by race. While <1% of non-Hispanic whites have experienced some measure of racism, this increases to 8.8% among non-Hispanic blacks. Perceived discrimination was associated with significantly greater odds of unmet medical needs in the adjusted, survey-weighted multivariate-adjusted model (adjusted odds ratio [OR] = 2.4 and 95% confidence interval [CI] = 1.2, 4.9) as well as in the CEM-model estimate (OR = 2.8 and 95% CI = 1.8, 4.0). Children who have experienced perceived discrimination had higher odds of unmet medical needs. Awareness of discrimination among children may help inform future intervention development that addresses unmet medical needs during childhood.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".