Targeting Determinants of Diabetes: A Move Toward An Applied Democracy
Bibliographic record
Abstract
The growing epidemic in Type II diabetes has disproportionately affected marginalized populations in the United States and has caused an uncontrollable and unsustainable increase in the cost of treating and managing diabetes. To date, there are no effective widespread treatments or interventions to reverse these trends. The answers, however, must be sought outside the traditional biomedical perspective not only to incorporate the political, social, historical, and economic disease determinants, but also to actually put them into practice. Over the past two decades institutions, organizations, and individuals are recognizing that concepts of health and disease have broader implications than we once thought. In 1986 the Regional Office for Europe of the World Health Organization (WHO) and the Ottawa Charter for Health Promotion identified nine conditions and resources that constitute health: peace, shelter, education, food, income, a stable ecosystem, sustainable resources, social justice, and equity. This definition acknowledges the diverse and multiple factors that contribute to the incidence and prevalence of disease in people. Determinants of disease have political, social, cultural, environmental, genetic, economic, historical, and individual origins. Health professionals seek long-term solutions to epidemics, like diabetes, by identifying and intervening in disease determinants in various populations. However, previous and current medical and public health interventions are patient-centered and fall short of addressing the multiple determinants acknowledged by the WHO in 1986. Given the current rising costs of health care and fiscal crises, U.S. policymakers and the health care industry are faced with placing limits on health care resources. Terms like cost-effectiveness, equity, and quality of care are factoring into how health care is delivered. This signifies a change from the biomedical patient-focused distribution of care to one concerning social justice and equity. A study performed in 1999 by Jeffery and French showed that intervention efforts targeting lifestyle changes and behavior modification were not sustainable over a three year time period. Additionally, Williamson has stated that community-based educational efforts are ineffective because behaviors are much more complex than researchers assume. By expanding the determinants of diabetes to reflect a more broadened definition of health, medical professionals can better understand the actual causes of disease in populations and, consequently, implement more effective interventions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".