The science of trust: future directions, research gaps, and implications for health and risk communication
Bibliographic record
Abstract
‘Trust is among the most important factors in human life, as it pervades’ all domains of society [1 Riedl R, Javor A. The biology of trust: integrating evidence from genetics, endocrinology, and functional brain imaging. J Neurosci Psychol Econ. 2012;5:63–91.[Crossref], [Web of Science ®] , [Google Scholar]] and related decision-making processes. This includes people’s trust in science, and in clinical and public health solutions. Unequivocally, community and patient trust are foundational to the adoption and maintenance of health-related behaviors, social norms, and policies. Yet, trust has to be earned and developed over time and through multiple interactions. Trust is about dialogue and human connection. It’s about listening and knowing that one interaction will not be enough to build trust. It is also influenced by a variety of social, economic, cultural, and political factors, past experiences, and the history of specific communities and patient groups. It should be at the core of the health and social systems with which people interact. More recently, trust in evidence-based information has also been affected by misinformation, not only on social media but also in a variety of community, institutional, and patient settings. Ultimately, we are in the midst of a global trust crisis that precedes the COVID-19 pandemic and is often rooted in the health, racial, and social inequities many groups experience [2 Schiavo R, Eyal G, Obregon R, Quinn SC, Riess H, Boston-Fisher N. The ‘Science of Trust’: future directions, research gaps, and implications for health and risk communication. Roundtable proceedings. J Commun Healthc: Strategies. Media Engagem Glob Health. 2022. doi:10.1080/17538068.2022.2121199. [Google Scholar]].
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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 | 0.046 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.016 | 0.032 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.016 | 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".