Evidence-based communication on climate change and health: Testing videos, text, and maps on climate change and Lyme disease in Manitoba, Canada
Bibliographic record
Abstract
Given the climate crisis and its cumulative impacts on public health, effective communication strategies that engage the public in adaptation and mitigation are critical. Many have argued that a health frame increases engagement, as do visual methodologies including online and interactive platforms, yet to date there has been limited research on audience responses to health messaging using visual interventions. This study explores public attitudes regarding communication tools focused on climate change and climate-affected Lyme disease through six focus groups (n = 61) in rural and urban southern Manitoba, Canada. The results add to the growing evidence of the efficacy of visual and storytelling methods in climate communications and argues for a continuum of mediums: moving from video, text, to maps. Findings underscore the importance of tailoring both communication messages and mediums to increase uptake of adaptive health and environmental behaviours, for some audiences bridging health and climate change while for others strategically decoupling them.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".