Integrating Narrative Pedagogy and Developmental Psychology: Using I Am Shakespeare to Teach Structural Health Inequities in Public Health Education
Bibliographic record
Abstract
This study evaluates the pedagogical impact of integrating the documentary I Am Shakespeare: The Henry Green Story into an undergraduate public health course focused on structural health inequities. Through narrative-based learning, students examined how social determinants, such as racism, poverty, educational access, and neighborhood disinvestment, shape individual and population health outcomes. The documentary provided an emotionally resonant entry point for applying frameworks like the Social Determinants of Health (SDOH) and the Social-Ecological Model (SEM), while also engaging students in complex themes such as hypermasculinity, trauma, and resilience. The study draws on student reflections, final papers, and survey data to assess how narrative-centered instruction supports systems thinking, cultural humility, and advocacy-oriented learning. In addition to these public health competencies, the analysis incorporates developmental psychology perspectives, emphasizing how late adolescence and emerging adulthood, critical periods for identity formation, emotional regulation, and cognitive growth, are shaped by exposure to structural violence and chronic stress. Students demonstrated increased awareness of the psychological toll of inequity, including its impact on self-efficacy, executive function, and interpersonal trust. Findings suggest that narrative-based learning promotes both conceptual mastery and personal growth, helping students integrate academic content with evolving ethical and professional identities. By bridging public health theory with lived experience, and aligning instruction with developmental and psychological milestones, this approach offers a multidimensional framework for preparing equity-minded public health professionals. The study highlights the value of interdisciplinary pedagogy in fostering critical reflection, empathy, and a deeper commitment to social transformation.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| 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".