Democratising clinical trials research to strengthen primary health care
Bibliographic record
Abstract
The World Health Assembly has called for clinical trials to be strengthened, with broader demographic and geographical inclusion of populations. The objective of this paper is to highlight the importance of rigorous evidence to maximise the health gains of primary health care, and to identify strategies for strengthening clinical trials in primary care. Clinical trials should evaluate interventions of all kinds, including preventive manoeuvres, diagnostics, health service research questions, behavioural and educational interventions, vaccines, therapeutics, and policies. Single question trials can be inefficient and seldom strengthen health systems. New approaches that develop or strengthen health research infrastructure and embed research in primary care will identify effective interventions faster, how to deliver them better, and more accurately determine to whom they should be applied. When patients and community members, together with researchers, contribute to conception, design, and delivery, research will result in more useful, relevant evidence. Traditional site-based recruitment (where the participant comes to the trial) can be complemented by approaches that give people the opportunity to contribute regardless of where they live and receive their health care (taking the trials to the people). However, this cannot be done until regulation is modernised to make it easier for health-care professionals, researchers, and research participants to co-design, deliver, and implement such trials, and to develop processes to coordinate and monitor progress against goals for budget shifts, delivery, engagement, trials activity, and impact. Strengthening primary care trials is especially important in those regions where primary care is most under-resourced and is key to pandemic preparedness. Not doing so risks widening inequities further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.100 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.008 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads 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".