Evaluating Health Literacy among Adolescent and Young Adult Pregnant Women from a Low-Income Area of Northeast Brazil
Bibliographic record
Abstract
Adequate health literacy is important for strong health outcomes during pregnancy, particularly among mothers with high risk of adverse outcomes related to pregnancy and childbirth. Understanding the health literacy of young pregnant women in low-income settings could support strategies to reduce adverse outcomes in this population. This exploratory study assessed the health literacy of young pregnant adolescents and young adults from a rural area in Northeast Brazil and associated factors such as socioeconomic conditions, adequacy of prenatal care, and social support from family and friends. In this cross-sectional study, 41 pregnant adolescents (13–18 years) and 45 pregnant adults (23–28 years) from the Rio Grande do Norte state, Brazil, were assessed regarding health literacy through the Short Assessment of Health Literacy for Portuguese-Speaking Adults (SAHLPA, score from 0–18, inadequate if <15). Income sufficiency, self-perceived school performance, compliance with recommendations for adequate prenatal care, and social support were also assessed. A linear regression analysis was conducted to evaluate the variables associated with the SAHLPA score. Ninety-five percent of the adolescents and 53.3% of the adults (p < 0.001) presented inadequate health literacy. Adolescent age (β − 3.5, p < 0.001), poorer self-perceived school performance (β − 2.8, p < 0.001), and insufficient income for basic needs (β − 2.8, p = 0.014) were associated with worse SAHLPA scores. Adolescent mothers have higher rates of inadequate health literacy in this population. Policies are needed to improve access to health information for young populations from rural low-income areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".