DESIGN IN PUBLIC HEALTH CRISES: THE POWER OF EMPATHY IN VISUAL STORYTELLING A MULTI-COUNTRY ANALYSIS OF COVID-19 PUBLIC HEALTH CAMPAIGNS
Bibliographic record
Abstract
This dissertation examines the role of storytelling during the COVID-19 public health crisis and its potential to foster empathy and action in a world increasingly marked by otherization, polarization, and anti-science sentiments. Using the S4E framework—State, Science, Storytellers, and Society—developed in this research, I explore the process of crafting public health campaigns and their capacity to elicit empathic (E) responses. Analyzing selected COVID-19 advertisements from the United States, Canada, Australia, New Zealand, Nigeria, and the United Kingdom, alongside interviews with public health storytellers from these countries, I investigate their tools and processes. Findings suggest that empathic storytelling in public health is shaped by how the State, Science, Storytellers, and Society in the S4E framework view the world. Key insights are synthesized into a strategy playbook, the 10 Ps of Empathy, which offers promising practices for future health crisis communications.
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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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".