Primary care: the sleeping giant of research delivery
Bibliographic record
Abstract
Primary care: the sleeping giant of research deliveryWe write in response to the recent editorial by Dickson and colleagues 1 advocating the expansion of research in primary care.We agree that the increasing transfer of clinical care from secondary to primary care, the frequency of consultation, and the increasing care of chronic disease by primary care mean that primary care should be the 'go to' setting for research studies.This is underpinned by the extensive clinical data held by GPs, which we are only just beginning to fully use for identification of potential participants.The authors refer to operationalisation of the 2021 National Institute for Health and Care Research (NIHR) Clinical Research Network research strategy.2 The strategy was set up to address a number of the issues highlighted by the authors, including funding to primary care from the 15 Local Clinical Research Networks (LCRNs), more sophisticated Participant Identification Centre (PIC) models, and formally involving primary care organisations in the strategic delivery of research locally.In 2021/2022 we were pleased to see that 51% of GP practices in England participated in CRN research, excluding those practices that only undertook PIC activity.We have successfully revised and implemented the PIC model for commercial research to make it more attractive to GP practices.In addition, all LCRNs now have primary care represented on their Partnership Groups.It is also promising to see that initial data from the first 6 months of the National Contract Value Review process show that, on average, commercial studies are achieving study set-up milestones 95 days quicker, which we plan to expand into primary care.There is more still to do and we agree that there is an urgent need for research governance support for primary care, which has been reduced in the move from clinical commissioning groups (CCGs) to integrated care boards (ICBs).We strongly believe that a greater collective focus between all the research infrastructure to expand primary care research is urgently needed, with the ultimate aim, as described by the authors, of making research business as usual.
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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 | 0.091 | 0.220 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.018 | 0.031 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.095 | 0.122 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".