Uncertainty communication, trust and health promotion
Bibliographic record
Abstract
Health promotion is more effective when health communicators are considered trustworthy. However, health communicators must often deal with uncertainties in the knowledge base on which they rely. In this commentary, we discuss the benefits of acknowledging uncertainty, with caveats and best practices to cultivate trust. We recommend determining the type of uncertainty involved and selecting appropriate communication approaches. We also advise that communicators emphasize the positive elements of the uncertainty, whenever possible, such as when it reflects a growing evidence base. Health promoters should consider the long-term outcomes of communicating uncertainty, as these may differ from the short-term outcomes. We identify knowledge gaps and areas ripe for future research. We also show that uncertainty can often be communicated without harming trust in the communicator, and that communicators should rely on evidence-based best practices. We aim to provoke further discussion on how uncertainty should be understood and framed in health promotion efforts, guiding communicators on how to maintain public trust amid unknowns.
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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 | 0.030 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.035 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".