Health literacy and uptake of annual physical checkups among emerging adults in the United States: Findings from the Behavioral Risk Factor Surveillance System
Bibliographic record
Abstract
Abstract Public health literature is replete with evidence on the determinants of preventive healthcare utilization. However, gap exists in the relationship between health literacy, a key social determinant of health, and annual physical checkups, especially among younger adults in the United States. This age group is one of the least likely to utilize such services for screening and prevention of diseases, which can have a significant impact on their long‐term health as they progress through the life course. Using the Andersen Healthcare Utilization framework, this study investigated the association between health literacy, an enabling factor, and uptake of annual physical checkups among emerging adults aged 18–29. A binary logistic regression model was employed to achieve the study objective using data from the 2016 Behavioral Risk Factor Surveillance System data ( N = 9515). Findings showed that 61% of young adults had physical checkups in the past year. After adjusting for predisposing, need, and other enabling factors, experiencing difficulties with oral and written health literacy and having difficulties obtaining medical information and advice were significantly associated with lower odds of physical checkups in the past year. These findings provide evidence for strategies like Healthy People 2030 that aim to increase preventive healthcare service utilization among emerging adults.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".