Risk communication: lessons from an ethnographic, pragmatic, and Canadian regulatory perspective
Bibliographic record
Abstract
In a regulatory context, it is important to understand how effective risk communication fits into the overall risk assessment, management, and decision-making process. This includes recognizing the intersections between risk analysis and the 3Ps: policy, politics, and publics, and understanding the barriers to effective communication. Risk communication is especially challenging when it requires the audience to follow and act on authoritative information or advice. Risk communicators must factor attributes such as risk perception, tolerance, and behaviors, and tailor the delivery of messages to diverse audiences. This paper captures the discourse from an intradepartmental workshop on risk communication with participants from Health Canada and the Public Health Agency of Canada. The workshop provided an opportunity to discuss and share references to existing frameworks, pertinent documents, and examples of effective risk communication strategies based on the authors' ethnographic and pragmatic experiences. The workshop aimed to strengthen risk communication by better understanding the value in collaborating with interdisciplinary teams, applying a systems thinking lens, and finding opportunities to experiment and evaluate risk communication strategies for regulatory purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.041 | 0.048 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".