The Future of Disease Control Priorities Comment on "Disease Control Priorities Third Edition Is Published: A Theory of Change Is Needed for Translating Evidence to Health Policy"
Bibliographic record
Abstract
The Disease Control Priorities (DCP) project has substantially influenced national and global health priorities since 1993. DCP's basic framework involves identification of disease burdens based on premature deaths and disability and application of the most cost-effective interventions to the largest burdens, taking into account local feasibility. The future impact of DCP will need to take into account growing national wealth and needs for endogenous capacity to design and implement evidence-based interventions, the rapid emergence of non-communicable disease (NCD), and the universal health coverage (UHC) agenda. This in turn requires three improvements to the DCP framework: greater local capacity, supported by a global effort to cost health interventions, stronger national and international technical capacity and networks, and the use of direct, versus modelled, mortality data to assign priorities and to assess progress. Properly done, DCP could be as important over the next 25 years as it has been in the past 25 years.
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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 | 0.016 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.100 | 0.088 |
| Insufficient payload (model declined to judge) | 0.017 | 0.019 |
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".