Health Product Risk Communication: Is the message getting through?
Bibliographic record
Abstract
Risk communication is an important component of improving the health and safety of Canadians. For numerous departments and agencies at all levels of government, as well as public and private organizations, effective risk communication can protect Canadians from preventable hazards. The Minister of Health, on behalf of Health Canada (the Sponsor), asked the Council of Canadian Academies (the Council) to provide an evidence-based and authoritative assessment of the state of knowledge on measurement and evaluation of health risk communication. This assessment focuses on identifying tools, evaluation methods, gaps in the literature, and barriers and facilitators to carrying out successful communication and evaluation activities. Specifically, this assessment examines the following questions: How can the effectiveness of health risk communications be measured and evaluated? • What types of instruments/tools are currently available for health risk communication? • What methodological best practices can be used to evaluate the reach, use and benefit of health risk communication? • What research could be done to inform the measurement of the effectiveness of risk communications? • What are the existing barriers to effective risk communications and what best practices exist to address these challenges? To address the charge, the Council assembled a multi-disciplinary panel of 11 experts (the Panel) from Canada and abroad. The Panel’s composition reflected a balance of expertise, experience, and demonstrated leadership in academic, clinical, and regulatory fields. Each member served as an informed individual, rather than as a representative of a particular discipline, patron, organization, or region.
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 | 0.051 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".