Paramedics working in general practice: a scoping review
Bibliographic record
Abstract
Background: The term ‘paramedic’ has traditionally related to a healthcare professional trained to provide pre-hospital emergency care; however, paramedics are increasingly taking on novel additional non-emergency roles. General practice is facing unprecedented demand for its services related to rising expectations, an aging society and increased prevalence of chronic disease. Paramedics may be recruited to work in general practice to meet some of these demands. We undertook a scoping review to map the current literature considering paramedics working in general practice and inform follow-on research. Methods: We employed the six-stage scoping review framework developed by Arksey and O’Malley. Our research question was ‘to identify the scope of practice, nature of training/qualifications, challenges faced, and impacts of paramedics working in general practice’. Results: After searching PUBMED (Medline, n = 487), EMBASE (n = 536) and the Cochrane Library (n = 0) in June 2020, we identified eleven full-text articles that met our inclusion criteria. The literature suggests that paramedics have diverse skills that enable roles within general practice, some of which are context specific. Additional training is considered necessary to facilitate the transition from emergency care to general practice. We found no research that quantitatively assessed the impact of paramedics working in general practice on healthcare expenditure or patient health outcomes. Conclusions: There is a paucity of empiric scientific literature considering paramedic working in general practice. Further research is needed to inform training pathways, the structure of clinical practice and to measure outcomes.
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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 | 0.026 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".