Creating a safety net for patients in crisis: paramedic perspectives towards a GP referral scheme
Bibliographic record
Abstract
An innovative policy implemented by a UK Ambulance Service allows paramedics to refer patients to a GP Acute Visiting Service scheme. Initial evidence suggests that this alternate route of care can decrease hospital admission rates, decrease A&E waiting time and provide substantial savings for the NHS. However, there are many unrecognised barriers to referral that are not captured by the quantitative analysis. The goal of this qualitative-observational study was to gain insight into the GP referral scheme from a paramedic's perspective. All notes were transcribed, coded and analysed using a Grounded Theory approach. Four main themes emerged: 1) barriers to referral including wait time, process, and lack of confidence, experience and training 2) approaching the patient with the GP referral scheme in mind 3) frustrations with GP decision making and 4) awareness/understanding of the scheme's impacts. This study provided valuable insight into the paramedic's perspective of the GP referral scheme. Maximising understanding of the scheme, investigating the GP's perspective in decision making and ensuring knowledge and accountability of paramedics, GPs and the public were identified as solutions to strengthen and increase referral rates and scheme success.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".