Applying a multi-scalar framework for teaching about the catastrophic public health situation in Palestine and beyond
Bibliographic record
Abstract
Schools of public health aim to promote global health equity, decolonize education, and foster respectful global collaboration. However, translating these values into practice, particularly when discussing global conflicts – including the urgent situation in Gaza and Palestine overall – presents a significant pedagogical challenge. Students are increasingly seeking to understand the root causes of such crises, and health-focused institutions are urged to call for an end to genocide as a moral imperative consistent with their mission. To guide educators in navigating these sensitive discussions, this Perspective article re-purposes the World Health Organization’s “Driving Forces-Pressures-States-Exposures-Effects-Actions” (DPSEEA) framework as a pedagogical tool. Here we adapt the “Actions” component of DPSEEA to reflect classroom teaching applications, transforming the framework into a novel heuristic for systematically analyzing the complex determinants of health in humanitarian crises. DPSEEA enables students to move beyond immediate causes to understand how socio-political, environmental, and structural factors intertwine to create health inequities. We first provide a detailed description of the content for each DPSEEA stage, offering the necessary context for analyzing the crisis in Palestine. This is followed by specific teaching suggestions. A sample concept map is provided to illustrate implementation. This approach provides a much-needed guide for addressing the crisis in Palestine, while introducing a powerful, adaptable model for understanding humanitarian crises more broadly. By applying a structural and systemic analysis of health determinants, the re-purposed DPSEEA framework equips public health students with the tools to address global health inequities with greater rigor, responsibility, and impact.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".