Understanding the contribution of UK public health research to clinical guidelines: a bibliometric analysis
Bibliographic record
Abstract
Background: There is an increasing need to understand the wider impacts of research on society and the economy. For health research, a key focus is understanding the impact of research on practice and ultimately on patient outcomes. This can be challenging to measure, but one useful proxy for changes in practice is impact on guidelines. Methods: The aim of this study is to map the contribution of UK research and UK research funders to the National Institute for Health and Clinical Excellence (NICE) public health guidelines, understanding areas of strengths and weakness and the level of collaboration and coordination across countries and between funders. The work consisted of two main elements: analysis of the references cited on NICE guidelines and interviews with experts in public health. Results: Across the papers cited on 62 NICE public health guidelines, we find that 28% of the papers matched include at least one UK affiliation, which is relatively high when compared to other health fields. In total, 165 unique funders were identified with more than three acknowledgements, based in 20 countries. 68% of papers which acknowledge funding cite at least one UK funder, and NIHR is the most highly cited funder in the sample. Conclusions: The UK makes an important contribution to public health research cited on NICE PH guidelines, although the research does not appear to be bibliometrically distinct from other research sectors, other than having a relatively low level of international collaboration. However, the extent to which NICE public health guidelines reflect practice at the local authority level is less clear. More research is needed to understand the sources of evidence to support public health decision making at the local level and how NICE guidance can be made more applicable, timely and accessible in this new context.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.093 | 0.620 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.182 | 0.326 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".