Geographic and Sociodemographic Variations in Prevalence of Mental Health Symptoms Among US Youths, 2022
Bibliographic record
Abstract
Objectives. To assess geographic and sociodemographic variations in prevalence of mental health symptoms among US youths. Methods. We analyzed data from the Household Pulse Survey, phases 3.5 and 3.6, between June 1 and November 14, 2022. The sample included 103 296 households with an estimated 190 017 youths younger than 18 years. We defined mental health symptoms based on parental responses and estimated prevalence by state and subgroups, including race/ethnicity, parental education, household income, housing tenure, household food sufficiency, and health insurance coverage. All analyses incorporated sampling weight. Results. An estimated 34.5% (95% confidence interval [CI] = 33.7%, 35.3%) of youths had parent-reported mental health symptoms. The prevalence of symptoms varied across states, ranging from 27.9% (95% CI = 23.8%, 32.0%) in Florida to 46.4% (95% CI = 41.9%, 50.9%) in New Hampshire. We observed variations by subgroup, with youths in households that did not pay rent reporting a prevalence of 43.8% (95% CI = 39.3%, 48.4%) and those experiencing food insufficiency reporting a prevalence of 56.0% (95% CI = 50.9%, 61.2%). Conclusions. There is an urgent need for attention to mental health challenges among youths, taking into account geographic and sociodemographic variations. (Am J Public Health. 2023;113(10):1116–1119. https://doi.org/10.2105/AJPH.2023.307355 )
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".