Social Determinants of Health and Perceived Cognitive Difficulties in High School Students in the United States
Bibliographic record
Abstract
OBJECTIVE: The United States Centers for Disease Control and Prevention (CDC) conducted the Adolescent Behavior and Experiences Survey (ABES) to examine disruption and adversity during the COVID-19 pandemic. We examined the association between social determinants of health (SDoH) and cognitive problems attributed to physical or mental health problems among high school students. METHOD: The ABES was an online survey. Perceived cognitive problems were assessed with the question: "Because of a physical, mental, or emotional problem, do you have serious difficulty concentrating, remembering, or making decisions?" A SDoH index was created by summing endorsements to 12 variables. RESULTS: Participants were 6,992 students, age 14-18, with 3,294 boys (47%) and 3,698 girls (53%). Many adolescents reported experiencing cognitive problems (i.e., 45%), with girls (56%) more likely to report cognitive difficulties than boys (33%) [χ2(1) = 392.55, p < 0.001]. Having poor mental health was strongly associated with cognitive problems in both girls [81%, χ2(1, 3680) = 650.20, p < 0.001] and boys [67%, χ2(1, 3267) = 418.69, p < 0.001]. There was a positive, linear association between the number of SDoH experienced and reporting cognitive problems. Binary logistic regressions were used to identify predictors of cognitive difficulty for both boys and girls (e.g., being bullied electronically, experiencing food insecurity during the pandemic, being treated unfairly because of their race or ethnicity, and being in a physical fight). CONCLUSIONS: A strikingly high proportion of adolescents reported experiencing problems with their cognitive functioning. After adjusting for current mental health problems, several SDoH remained associated with adolescents' reported cognitive difficulties, including experiencing racism, bullying, parental job loss, and food insecurity.
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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 | 0.000 |
| 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 | 0.000 | 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".