DESIGN IN PUBLIC HEALTH CRISES: THE POWER OF EMPATHY IN VISUAL STORYTELLING A MULTI-COUNTRY ANALYSIS OF COVID-19 PUBLIC HEALTH CAMPAIGNS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it