Learning from the pandemic: Building capacity for risk communication in the Canadian federal health portfolio
Bibliographic record
Abstract
SETTING: The federal health portfolio has had a risk communications framework in place since 2006; however, the COVID-19 pandemic pushed the capacity of this plan and the need for communications resources to new levels. Health communicators in the public service face significant challenges: a fragmented mediascape, changes to how people seek and use information, the proliferation of misinformation and disinformation, declining trust in public institutions, and the politicization of science, to name just a few. It has never been more important for health authorities to communicate clearly, consistently, effectively, and from an evidence-based position. INTERVENTION: This report describes one aspect of how the federal health portfolio has been addressing these challenges. As part of a recent capacity-building initiative, 67 public servants working in health communications participated in a four-part, half-day, advanced seminar series at Carleton University in June 2023. Each session featured an interactive presentation from a leading scholar and/or local practitioner with real-world scenario exercises designed to put their learning into practice. The series explored issues in trust and transparency, algorithmic control and mis- and disinformation, media relations, and risk communication for equity-deserving populations. OUTCOMES: At the conclusion of the program, participants were given tools to (1) identify challenges to effective communication brought by a rapidly evolving media environment in which skepticism and misinformation often run rampant; (2) examine how key metrics and behavioural indicators on social media platforms demand different responses from health organizations and agencies who are monitoring and managing social media; (3) consider challenges for health communicators who must serve the public during health crises while also reinforcing public trust in their institutions; and (4) develop successful risk communication strategies for equity-deserving communities by considering specific information needs and tailored dissemination methods to reach the intended audience. Participants expressed high levels of satisfaction in the quality of the training and overwhelmingly reported that it would positively impact their daily work. IMPLICATIONS: The training program was an innovative and successful initiative to improve knowledge of current priority topics and best practices in risk communication. It illustrated the benefits of continued professional learning, the importance of university-public service partnerships, and how capacity building requires ongoing resource commitments and engaged support from senior management. The program, along with other risk communication training that is currently being implemented, is part of the investment in long-term professional development of risk communicators in the health portfolio.
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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 | 0.023 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.011 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".