Disease Control Priorities Third Edition: Time to Put a Theory of Change Into Practice 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 program (DCP) has pioneered the use of economic evidence in health. The theory of change (ToC) put forward by Norheim is a further welcome and necessary step towards translating DCP evidence into better priority setting in low- and middle-income countries (LMICs). We also agree that institutionalising evidence for informed priority-setting processes is crucial. Unfortunately, there have been missed opportunities for the DCP program to challenge ill-judged global norms about opportunity costs and too little respect has been shown for the wider set of local circumstances that may enable, or disable, the productive application of the DCP evidence base. We suggest that the best way forward for the global health community is a new platform that integrates the many existing development initiatives and that is driven by countries' asks.
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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 | 0.034 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.102 | 0.130 |
| Insufficient payload (model declined to judge) | 0.017 | 0.016 |
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".