Teaching Health Equity Through Narrative Media: The Pedagogical Impact of The Color of Care in Public Health Education
Bibliographic record
Abstract
The COVID-19 pandemic exacerbated deep-rooted health disparities in the United States, particularly among marginalized communities facing systemic barriers such as limited healthcare access, economic instability, and environmental injustices. Black, Hispanic, and Indigenous populations experienced disproportionately high rates of infection, hospitalization, and mortality, underscoring the profound impact of structural inequities on health outcomes. Understanding these disparities is essential for public health students, yet traditional teaching methods often fail to convey their real-world implications. Integrating media-based narratives, particularly documentaries, into public health education offers a compelling way to illustrate how structural factors shape health outcomes. This study evaluates the impact of The Color of Care, a documentary that examines the disproportionate burden of COVID-19 on communities of color, as a pedagogical tool in PBHL 6000: Community Engagement, Equity, and Justice. Through analysis of student reflections, coursework, and survey responses, findings indicate that the documentary enhanced students' comprehension of social determinants of health, deepened emotional engagement, and fostered critical thinking about systemic racism and health inequities. Results show a significant increase in students’ confidence in applying public health frameworks and a greater commitment to health advocacy. This study contributes to ongoing discussions on the role of media narratives in public health education, highlighting their potential to contextualize abstract theories, foster cultural competence, and inspire systemic change. The findings support the integration of documentary-based learning to enhance public health curricula and better prepare students for addressing health disparities in their professional careers.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".