Narrative Pedagogy in Public Health: Teaching Systemic Inequities Through the Short and Tragic Life of Robert Peace
Bibliographic record
Abstract
This study explores the impact of integrating The Short and Tragic Life of Robert Peace into the undergraduate public health course PBHL 2950: Disparities in Health as a pedagogical tool to enhance student understanding of systemic inequities. Traditional lecture-based methods often fail to effectively convey complex concepts such as social determinants of health (SDOH), systemic racism, and economic disparities. Narrative-based learning, however, has been shown to deepen student engagement by humanizing statistical data and theoretical constructs. Using a mixed-methods approach, this study assesses how engagement with Robert Peace’s life story influences students’ comprehension of public health frameworks, including the Social-Ecological Model (SEM) and SDOH, and their ability to critically analyze structural inequities. Findings indicate that students demonstrated significant improvement in their ability to identify systemic health disparities, apply public health theories, and engage in policy discussions. Qualitative analysis of student reflections revealed increased empathy, enhanced systems thinking, and a stronger motivation for public health advocacy. However, students faced challenges in developing actionable, multi-tiered public health interventions, underscoring the need for structured guidance on intervention design. The study suggests that integrating real-world narratives into public health curricula can bridge the gap between theory and lived experience, preparing students with the critical thinking skills necessary for addressing health disparities. These findings contribute to ongoing discussions on innovative teaching strategies that cultivate cultural competence, policy awareness, and advocacy-oriented thinking in future public health professionals.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".