When They See Us: Leveraging Narrative Media to Explore the Intersections of the Social Determinants of Health, the Social Ecological Model, and the Criminal Justice System
Bibliographic record
Abstract
This study explores the use of When They See Us, a Netflix miniseries depicting the wrongful convictions of the Central Park Five, as a teaching tool in an undergraduate public health course. The series was integrated into the curriculum to help students examine the social determinants of health (SDOH) and the social-ecological model (SEM) through the lens of systemic racism and the criminal justice system. As part of the course, students analyzed the series using public health frameworks and proposed evidence-based interventions across multiple levels of influence. A mixed-methods approach was used to assess the impact of the assignment, drawing on rubric-based evaluations of student papers, thematic analysis of reflection responses, and post-course survey data. Quantitative results showed strong student performance in applying theoretical models, identifying structural drivers of health disparities, and developing practical, multi-level solutions. Qualitative findings pointed to increased emotional engagement, empathy, and critical reflection on the relationship between injustice, trauma, and health. Students reported that the series made course concepts more tangible and deepened their understanding of the broader systems that shape health outcomes. While some found the material emotionally challenging or entered with limited background knowledge, the overall findings suggest that narrative media can be a powerful tool for public health education. This approach not only supports the application of theory to real-world issues but also encourages critical thinking and prepares students to engage in equity-focused public health practice.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".