Chronic Diseases: Chronic Diseases and Development 1 Raising the priority of preventing chronic diseases: a political process
Bibliographic record
Abstract
Chronic diseases especially cardiovascular diseases, diabetes cancer and chronic obstructive respiratory diseases, are neglected globally despite growing awareness of the serious burden that they cause Global and national policies have failed to stop, and in many cases have contributed to, the chronic disease pandemic Low cost and highly effective solutions for the prevention of chronic diseases are readily available the failure to respond is now a political rather than a technical issue We seek to understand this failure and to position chronic disease centrally on the global health and development agendas To identify strategies for generation of increased political priority for chronic diseases and to further the involvement of development agencies we use an adapted political process model This model has previously been used to assess the success and failure of social movements On the basis of this analysis we recommend three strategies reframe the debate to emphasise the societal determinants of disease and the inter relation between chronic disease, poverty, and development, mobilise resources through a cooperative and inclusive approach to development and by equitably distributing resources on the basis of avoidable mortality and build on emerging strategic and political opportunities such as the World Health Assembly 2008-13 Action Plan and the high level meeting of the UN General Assembly in 2011 on chronic disease Until the full set of threats which-include chronic disease-that trap poor households in cycles of debt and illness are addressed, progress towards equitable human development will remain inadequate
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".