Protocol for the impact of paramedics in NHS primary care: application of realist approaches to improve understanding and support intelligent policy and future workforce planning
Bibliographic record
Abstract
INTRODUCTION: In the United Kingdom, changing demands on ambulance services has caused a change in what is expected of a paramedic. As well as advanced life support, paramedics now need to be skilled in managing a range of urgent case presentations, with emphasis on treat-at-scene. The change in the scope of work paramedics can undertake has established their role within primary care. However, as paramedics transition to roles within primary care, their knowledge and skillset will undoubtedly need to change. The current opportunities for paramedics' employment in primary care require careful evaluation. In order to contribute to patients' and the NHS' primary care agenda, evidence must be generated to show how and why these changes would work, for whom, in what context and to what extent. METHODS AND ANALYSIS: The purpose of this research is to produce findings that will improve our understanding of the ways in which (i.e. how, why and in what contexts) paramedics impact on the primary care workforce. A theory-driven approach to evidence-synthesis will be conducted in a realist review, to produce a programme theory. This programme theory will be tested using empirical data collected through a realist evaluation. Survey and interview data will be collected from paramedics working in primary care, general practitioners and patients to assess under which contexts, and by which mechanisms, paramedics are working in primary care, and thus test the programme theory. Based on the findings, we will be able to highlight the role of paramedics in primary care, as well as how they operate and under what conditions. ETHICS AND DISSEMINATION: Formal ethics review is not required for the review, as it is secondary research, but will be sought for the evaluation. Findings will be disseminated in a peer-reviewed journal, at national and international conferences and to relevant professional associations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".