Imbalances in funding for clinical and public health research in the UK: can NICE research recommendations make a difference?
Bibliographic record
Abstract
National Institute for Health and Clinical Excellence (NICE) recommendations for further research often focus on access, prevention and health promotion, and include population groups traditionally excluded from clinical trials, such as children and minority ethnic groups. NICE and its advisory bodies are starting to work with the National Institute for Health Research to promote research into the clinical and cost-effectiveness of public health interventions. More streamlined interaction between public funders and policy makers could help drive public R&D (Research & Development) investment towards under-researched areas such as prevention, equity and access, and strengthen the evidence base that underpins NICE guidance and broader clinical and public health practice in the UK.
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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 | 0.409 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.035 | 0.043 |
| Open science | 0.013 | 0.017 |
| Research integrity | 0.055 | 0.033 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".