Understanding the mechanisms underpinning health inequalities: lessons from economics
Bibliographic record
Abstract
The two most important books relating to health promotion and public health published in the last five years were not written by health promotion or public health specialists. They were written by economists! The two texts in question are Capital in the Twenty-First Century by Thomas Piketty (1) and The Global Minotaur: America, Europe and the Future of the Global Economy written by Yannis Varoufakis (2). Both books have been widely cited and discussed in public affairs, with both authors achieving something like a cult status: Piketty as a high-profile French intellectual and Varoufakis, albeit briefly, as the Greek Finance Minister responsible for negotiations with the EU about the Greek bailout. These books have profound implications for health promotion and public health. In both books, there are two different ideas which deserve our attention. One is the gloomy prognosis for the future of global public health, which follows from their economic analyses. The second is what we may learn about the detailed methods used to understand the mechanisms driving global health inequalities. It has been a conventional wisdom, at least since the arrival of the New Public Health in the 1980s, that there are underlying structural factors which impact the health of the public. The Ottawa Charter (3), with its emphasis on peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice and equity, for example, set out the stall for describing health and its determinants within a framework that went well beyond the thenconventional biomedical model of disease and of clinical interventions. The Ottawa Charter (3) made clear that health promotion policy combines diverse determinants, including legislation, fiscal measures, taxation and organizational structures. It argued that coordinated action leads to policies that foster greater equity (3). This very clear programmatic statement set a framework for much subsequent
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".