NICE's social value judgements about equity in health and health care
Bibliographic record
Abstract
The National Institute for Health and Clinical Excellence (NICE) routinely publishes details of the evidence and reasoning underpinning its recommendations, including its social value judgements (SVJs). To date, however, NICE's SVJs relating to equity in the distribution of health and health care have been less specific and systematic than those relating to cost-effectiveness in the pursuit of improved total population health. NICE takes a pragmatic, case-based approach to developing its principles of SVJ, drawing on the cumulative experience of its advisory bodies in making decisions that command respect among its broad range of stakeholders. This paper aims to describe the SVJs about equity in health and health care that NICE has hitherto used to guide its decision making. To do this, we review both the general SVJs reported in NICE guidance on methodology and the case-specific SVJs reported in NICE guidance about particular health care technologies and public health 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 | 0.019 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".