Health literacy and the Unified Health System across Brazil: Nutritionists’ perspective
Bibliographic record
Abstract
Introduction: Brazil has acknowledged the importance of health literacy at all levels of healthcare. There is a growing demand for nutritionists’ expertise in many areas of practice, particularly in the public health system, to respond to the population’s needs. Methods: Consultations were held from September to December 2023 in Fortaleza, Brazil. Eleven nutritionists and four nutrition students participated in face-to-face and online consultations. The sessions were audio recorded, transcribed, and analyzed using thematic analysis. The coding was guided by the ideas of organizational structural response to health literacy. Findings: Consultees criticized their traditional biomedical training, which contained little emphasis on communication and health literacy, and pointed to structural barriers hindering patient-centered care. When discussing tertiary health services, consultees identified better flows of information and greater access to technological equipment and materials that foster health literacy. Low-cost strategies—such as use of WhatsApp groups, bedside visuals, and mobile apps—have the potential to enhance patients’ health literacy. Implications for international health, policy, and practice: Measuring health literacy can inform other professional contexts, particularly those in middle- and low-income countries. Strategies to make health literacy a key tool in promoting professional practice include investing in continuing education, reviewing curricula, and expanding resources. Conclusion: Healthcare professionals are ready to integrate health literacy into public healthcare systems, but there are gaps in the infrastructure and in conceptions of nutritionists’ role.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".