62 (15A) Examining the associations between social determinants of health and lifetime history of parent-reported concussion in children and adolescents in the United States
Bibliographic record
Abstract
Purpose Social determinants of health (SDoH) are socioeconomic and environmental factors that influence health and wellbeing. We examined the association between SDoH and lifetime, parent-reported concussion history among youth in the United States.Methods Participants were parents/caregivers of 37,910 children and adolescents (ages 5–17) from the National Survey of Children’s Health conducted in 2022. Demographic variables analyzed included sex, age group, race, and ethnicity. SDoH variables analyzed included primary language spoken at home, parental level of education, low income, difficulty meeting basic needs, food insufficiency, needed healthcare not received, current health insurance, parental mental health, and sports participation. A multivariate logistic regression examined associations of SDoH and demographic variables with lifetime concussion history.Results Significant independent predictors of higher lifetime history of concussion included male sex (Odds Ratio [OR]=1.30), older age (OR=2.84), sports participation (OR=1.70), having difficulty covering the basics (OR=1.36) including food and housing on family income, food insufficiency (OR=1.14), needing healthcare not received (OR=2.04), and worse parental mental health (OR=1.58). Significant independent predictors of lower lifetime concussion history were not speaking English as the primary language at home (OR=0.24), lower level of parental education (OR=0.86), and identifying as Black (OR=0.44) or Asian (OR=0.38).Conclusions Some SDoH, such as not speaking English as the primary language at home, were associated with a lower lifetime history of concussion, possibly because of lower health literacy. Better understanding the interaction between sociodemographic factors and pediatric concussion might improve surveillance efforts and access to healthcare, particularly among marginalized or vulnerable populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".