EDUCATIONAL RADIO AS A MEDIATOR BETWEEN COMMUNICATION, HEALTH AND EDUCATION: REFLECTIONS FROM A REGIONAL EXPERIENCE
Bibliographic record
Abstract
This article proposes a reflection on the role of the mass media (MCM), with a focus on educational radios, in the context of the Brazilian Health System. Social participation, one of the central pillars of the system, presupposes the involvement of society in the allocation of resources and definition of priorities in different spheres. Educational radios emerge as mediating tools between health services and the population, promoting education and the exercise of citizenship. The study explores the impact of new information and communication technologies on the reconfiguration of media spaces and their implications for public health. In addition, it highlights the importance of disseminating health information as a right guaranteed by the 1988 Constitution. Using the educational radio station Cultura FM 99.3 MHz as a case study, we analyzed how this station operates in Nova Alta Paulista-SP, promoting social integration and awareness. The intersection between Communication and Health, based on practices such as those of the Ottawa Charter, is approached as strategic to expand the frontiers of public health. The article concludes by emphasizing the need for a continuous dialogue between the fields of Communication and Public Health, aiming to strengthen educational actions and democratize access to quality information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".