Leveraging national and global political determinants of health to promote equity in cancer care
Bibliographic record
Abstract
Health and politics are deeply intertwined. In the context of national and global cancer care delivery, political forces-the political determinants of health-influence every level of the cancer care continuum. We explore the "3-I" framework, which structures the upstream political forces that affect policy choices in the context of actors' interests, ideas, and institutions, to examine how political determinants of health underlie cancer disparities. Borrowing from the work of PA Hall, M-P Pomey, CJ Ho, and other thinkers, interests are the agendas of individuals and groups in power. Ideas represent beliefs or knowledge about what is or what should be. Institutions define the rules of play. We provide examples from around the world: Political interests have helped fuel the establishment of cancer centers in India and have galvanized the 2022 Cancer Moonshot in the United States. The politics of ideas underlie global disparities in cancer clinical trials-that is, in the distribution of epistemic power. Finally, historical institutions have helped perpetuate disparities related to racist and colonialist legacies. Present institutions have also been used to improve access for those in greatest need, as exemplified by the Butaro Cancer Center of Excellence in Rwanda. In providing these global examples, we demonstrate how interests, ideas, and institutions influence access to cancer care across the breadth of the cancer continuum. We argue that these forces can be leveraged to promote cancer care equity nationally and globally.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".