Mídia e cultura jornalística na comunicação em saúde
Bibliographic record
Abstract
This paper examines the media and journalistic culture in health communication by focusing more specifically on the ways journalists deal with health in the media. The analysis is based on several studies carried out with journalists and various population groups in Canada and Brazil. This paper demonstrates that there is a certain consistency in the journalistic treatment of healthcare and that this treatment is not always to the benefit of society. The profession of journalism is changing rapidly: the rapid flow of information, the abundant supply of information, the pace of news production, the place of citizens’ demands are some of the phenomena that have transformed the production of information carried out by journalists. Besides, people tend to resort more to media than to healthcare professionals to obtain information about risks and are therefore exposed to social norms regarding health. Considering the influence of media in health communication, this paper questions the media and journalistic culture through an ethical lens by addressing the journalist’s responsibility regarding media coverage of health, production of the news (source, treatment and tone), convergence and their health effect on the population. As main findings, we point out that journalism needs to invest in more effective health communication, which is committed to the well-being of citizens, through treatment and detailing of information that allow the support of the decision-making process by the population.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".