The empowering role of health literacy in combatting fake news, misinformation and infodemics
Bibliographic record
Abstract
Abstract Background In times of the rapid digital transformation, people need to acquire specific knowledge, skills and attitudes to deal with data, digital information and technology. Such skills are particularly important in times of crisis, as the COVID-19 pandemic has shown. Alongside the pandemic, a so called “infodemic” has emerged, i.e., an overabundance of information, accompanied by misinformation, which impedes sound decision-making processes, affects health literacy (HL) and impacts public health. However, while misinformation poses a well-known threat to our health and well-being, we still lack viable concepts and approaches to satisfactorily solving this issue. Thus, we aimed at reviewing the concept of HL in view of the infodemic and health-related challenges. Methods In a 12-month participatory process in Switzerland in 2023, we investigated the empowering role of HL in light of the polycrisis. On behalf of the Swiss Federal Office of Public Health, we conducted a literature review on existing HL definitions. Then, we conducted 6 expert interviews and 2 focus group interviews with other 10 experts in HL and associated domains, which guided and informed the review process. Results As a result, HL can be understood as a bundle of competencies to proactively deal with health-related information, services, and challenges and thereby empowers people to manage their and other’s health and well-being. Thus, HL empowers people to better manage digital information and services and promotes critical thinking. This in turn is necessary to assess information quality, uncover misinformation and to adequately manage health data and information. Conclusions HL represents a crucial prerequisite for individuals, professionals, and decision-makers to find trustworthy health information, to reflect upon the quality and credibility of sources and content, and to understand the complex interrelations of the determinants of health and is therefore crucial for public health. Key messages • Health literacy empowers people to adequately deal with fake news, misinformation and infodemics. • Strengthening health literacy offers great potential for public health to promote critical thinking and to rebuild trust.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".