Health Research Policy and Systems BioMed Central Editorial Building health research systems to achieve better health
Bibliographic record
Abstract
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Health research systems can link knowledge generation with practical concerns to improve health and health equity. Interest in health research, and in how health research systems should best be organised, is moving up the agenda of bodies such as the World Health Organisation. Pioneering health research systems, for example those in Canada and the UK, show that progress is possible. However, radical steps are required to achieve this. Such steps should be based on evidence not anecdotes. Health Research Policy and Systems (HARPS) provides a vehicle for the publication of research, and informed opinion, on a range of topics related to the organisation of health research systems and the enormous benefits that can be achieved. Following the Mexico ministerial summit on health research, WHO has been identifying ways in which it could itself improve the use of research evidence. The results from this activity are soon to be published as a series of articles in HARPS. This editorial provides an account of some of these recent key developments in health research systems but places them in the context of a distinguished tradition of debate about the role of
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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 | 0.025 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
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".