Cost‐effective public health guidance: asking questions from the decision‐maker's viewpoint
Bibliographic record
Abstract
In February 2004, in his assessment of the long-term financial viability of the NHS, Derek Wanless recommended the use of 'a consistent framework, such as the methodology developed by NICE, to evaluate the cost-effectiveness of interventions and initiatives across health care and public health'. One year later public health was added to NICE's remit and the new National Institute for Health and Clinical Excellence (NICE) was established, with amended statutory instruments to permit consideration of broader public sector costs when developing cost-effective guidance for public health. With the principle of 'a consistent framework' put forward by Wanless as the starting point, this paper provides an insight into the most challenging aspects of applying the principles of cost-effectiveness analysis in the public health context from the policymaker's perspective. It reflects on the long-term consequences of taking on responsibility for producing public health guidance on the Institute's overall approach to guidance development and describes the tension between striving for consistency and cross-evaluation comparability while ensuring that the methodological tools used are fit for the purpose of developing public health guidance.
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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 | 0.201 | 0.417 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.022 | 0.034 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.043 | 0.045 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".