New Models of Emergency Prehospital Care That Avoid Unnecessary Conveyance to Emergency Department: Translation of Research Evidence into Practice?
Bibliographic record
Abstract
BACKGROUND: Achieving knowledge translation in healthcare is growing in importance but methods to capture impact of research are not well developed. We present an attempt to capture impact of a programme of research in prehospital emergency care, aiming to inform the development of EMS models of care that avoid, when appropriate, conveyance of patients to hospital for immediate care. METHODS: We describe the programme and its dissemination, present examples of its influence on policy and practice, internationally, and analyse routine UK statistics to determine whether conveyance practice has changed. RESULTS: The programme comprises eight research studies, to a value of > £4 m. Findings have been disseminated through 18 published papers, cited 274 times in academic journals. We describe examples of how evidence has been put into practice, including new models of care in Canada and Australia. Routine statistics in England show that, alongside rising demand, conveyance rates have fallen from 90% to 58% over a 12-year period, 2,721 million fewer journeys, with publication of key studies 2003-2008. COMMENT: We have set out the rationale, key features, and impact on practice of a programme of publicly funded research. We describe evidence of knowledge translation, whilst recognising limitations in methods for capturing impact.
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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 | 0.304 | 0.598 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".