A realist evaluation to explain and understand the role of paramedics in primary care
Bibliographic record
Abstract
BACKGROUND: In response to the unsustainable workload and workforce crises in primary care, paramedics (with their generalist clinical background acquired from ambulance service experience) are increasingly employed in primary care. However, the specific contribution paramedics can offer to the primary care workforce has not been distinctly outlined. We used realist approaches to understand the ways in which paramedics impact (or not) the primary care workforce. METHODS: A realist evaluation was undertaken, consisting of three independent but inter-related research studies: In WP1, a mixed-methods cross-sectional survey of paramedics in primary care in the UK was conducted to comprehend the existing practices of paramedics within the NHS. WP2 involved an analytic auto-netnography, where online conversations among paramedics in primary care were observed to understand paramedics' perceptions of their role. WP3 utilised focused observations and interviews to delve into the impact of paramedics on the primary care workforce. This comparative study collected data from sixty participants across fifteen sites in the UK, and twelve participants across three sites in a specific region in Canada, where Community Paramedicine is well established. RESULTS: The culmination of findings from each phase led to the development of a final programme theory, comprising of 50 context-mechanism-outcome configurations (CMOCs) encompassing three conceptual categories: Expectations associated with paramedics in primary care, the transition of paramedics into primary care roles, and the roles and responsibilities of paramedics in primary care. CONCLUSIONS: Our realist evaluation used a mixed-method approach to present empirical evidence of the role of paramedics in primary care. It offers insights into factors relating to their deployment, employment, and how they fit within the wider primary care team. Based on the evidence generated, we produced a series of practice implementation recommendations and highlighted areas for further research.
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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 | 0.001 | 0.001 |
| 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".