Health Information and Misinformation: A Framework to Guide Research and Practice
Bibliographic record
Abstract
When facing a health decision, people tend to seek and access web-based information and other resources. Unfortunately, this exposes them to a substantial volume of misinformation. Misinformation, when combined with growing public distrust of science and trust in alternative medicine, may motivate people to make suboptimal choices that lead to harmful health outcomes and threaten public safety. Identifying harmful misinformation is complicated. Current definitions of misinformation either have limited capacity to define harmful health misinformation inclusively or present a complex framework with information characteristics that users cannot easily evaluate. Building on previous taxonomies and definitions, we propose an information evaluation framework that focuses on defining different shapes and forms of harmful health misinformation. The framework aims to help health information users, including researchers, clinicians, policy makers, and lay individuals, to detect misinformation that threatens truly informed health decisions.
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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 | 0.145 | 0.092 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.035 | 0.016 |
| Science and technology studies | 0.014 | 0.074 |
| Scholarly communication | 0.032 | 0.038 |
| Open science | 0.010 | 0.019 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".