Engaging interested parties to optimize wildfire smoke communication in Canada: challenges with initiating change
Bibliographic record
Abstract
Background In February 2022, an online Wildfire Smoke Communication Workshop series identified priorities and strategies to improve wildfire smoke communication in Canada. We evaluated the engagement methods, the workshop series and workshop summary report, to determine if participants/organizations initiated changes identified in the workshop to optimize wildfire smoke communication plans. Methods Three evaluation surveys were developed using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework dimensions and PRISM (Practical, Robust, Implementation, and Sustainability Model) contextual domains to measure the engagement impact. Surveys 1, 2, and 3 were disseminated to workshop participants between February 2022 (post-workshop series), May 2022 (pre-wildfire season), and September 2022 (post-wildfire season). Likert survey responses were analyzed descriptively using means and standard deviations. Open-ended written responses were analyzed using deductive reasoning and response proportions. Results Of 69 workshop participants, 28, 19, and 13 responded to surveys 1, 2, and 3, respectively. Workshop participation helped survey 1 respondents consider optimizing wildfire smoke communication (M = 3.93, SD = 0.88). Workshop participation and the summary report helped survey 2 respondents consider new actions to optimize wildfire smoke communication (M = 3.84, SD = 0.74). The most intended action in survey 2 (68%, n = 13) and the most common action taken in survey 3 (62%, n = 8) was to simplify message content. The primary limitation to optimization was capacity. Conclusion The engagement methods, particularly the summary report, were beneficial for organizations to take action to optimize wildfire smoke communication in Canada. Future engagement methods should examine persisting system-level issues and capacity limitations as they undermine the ability to optimize wildfire smoke communication in Canada.
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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".