Como as redes sociais influenciam a tomada de decisões sobre saúde: um estudo sobre letramento informacional em saúde e comunicação
Bibliographic record
Abstract
How Social Networks Influence Health Decision-Making: A Study on Health Information Literacy and CommunicationRESUMOA contemporaneidade trouxe mudanças significativas na forma como as pessoas se comunicam e se informam, especialmente sobre temas relacionados à saúde, considerando o que foi vivenciado durante a pandemia de COVID-19, onde se configurou uma epidemia de desinformação (Cinelli et al., 2020). Neste trabalho discutimos a Comunicação em Saúde (Thomas, 2006; Araújo e Cardoso, 2014; Nardi et al., 2018) a partir dos modelos comunicacionais clássicos (McQuail, 2003; Sousa, 2006; Serra, 2007; Martino, 2013) e vislumbramos seu papel em uma sociedade, cada vez mais dependente das redes sociais (Oliveira, 2014). Ponderamos sobre o papel da mídia e do jornalismo, como demandante de ações na promoção à saúde conforme preconiza a Cartas de Ottawa, de 1986. A partir das noções de Letramento Informacional (Gasque, 2012; 2020), Letramento/Literacia em Saúde (OMS, 2021; Peres, Rodrigues e Silva, 2021; Zarcadoolas, Pleasant e Greer, 2005), Letramento Informacional em Saúde (LIS) (Medical Libray Association, 2011; Niemelä et al, 2012), nos propusemos investigar as habilidades individuais em reconhecer; identificar; utilizar; avaliar; analisar e compreender informações em saúde e com elas tomar decisões (OMS, 2021) por meio de pesquisa on-line com 220 respondentes, constatamos a dificuldade no reconhecimento de fontes confiáveis no meio digital e a confiança do público em meios tradicionais como a televisão e os sites de notícias quando se trata de saúde, por essa razão também analisamos notícias veiculadas no mês de novembro de 2023 na editoria de saúde dos principais sites de notícias brasileiros, usando como base uma adaptação do protocolo da Rede Ibero-americana de Monitoramento e Capacitação em Jornalismo Científico (Massarani e Ramalho, 2012).ABSTRACTContemporaneity has brought significant changes in the way people communicate and inform themselves, especially on health-related topics, considering what was experienced during the COVID-19 pandemic, where an epidemic of misinformation was configured (Cinelli et al., 2020). In this paper, we discuss Health Communication (Thomas, 2006; Araújo and Cardoso, 2014; Nardi et al., 2018) based on classical communication models (McQuail, 2003; Sousa, 2006; Serra, 2007; Martino, 2013) and we glimpse its role in a society increasingly dependent on social networks (Oliveira, 2014). We pondered on the role of the media and journalism as a demand for actions in health promotion as recommended by the Ottawa Charters of 1986. Based on the notions of Information Literacy (Gasque, 2012; 2020), Health Literacy/Literacy (WHO, 2021; Peres, Rodrigues and Silva, 2021; Zarcadoolas, Pleasant & Greer, 2005), Health Information Literacy (HIL) (Medical Libray Association, 2011; Niemelä et al, 2012), we set out to investigate individual abilities to recognize; identify; use; evaluate; analyze and understand health information and make decisions with it (WHO, 2021) through an online survey with 220 respondents, we found the difficulty in recognizing reliable sources in the digital environment and the public's trust in traditional media such as television and news sites when it comes to health, for this reason we also analyzed news published in the month of November 2023 in the health section of the main news sites Brazilians, based on an adaptation of the protocol of the Ibero-American Network for Monitoring and Training in Scientific Journalism (Massarani and Ramalho, 2012). Key words: Information Literacy; Health Information Literacy; Health Communication; Social Media.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".