The impact of paramedics working in primary care teams on other professionals and patient experiences: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Paramedics are among the professional groups identified in recent policy initiatives aimed at addressing the unsustainable workload and workforce crises in primary care. Their support aims to enhance patient access to care and alleviate the burden of workload pressures. AIM: To explore the impact of paramedics working in primary care on primary care teams and the experiences of patients who have a clinical consultation with a paramedic in primary care. DESIGN & SETTING: A qualitative study using focused observations and interviews, involving 15 geographically dispersed sites across the UK. METHOD: Data were collected between May 2022 and January 2023, incorporating 60 semi-structured interviews and 60 hours of observations of paramedics. Transcripts were thematically analysed. RESULTS: Patients, GPs, and other staff in primary care perceive that the paramedic role enhances healthcare availability in primary care by increasing workforce capacity. This is especially prevalent when paramedics work in a clinical capacity that complements the GP role. However, successful integration into the primary care team relies on paramedics having sufficient clinical experience and receiving clinical supervision from GPs. Patients are trusting of the paramedic role when they have positive clinical consultations. CONCLUSION: Paramedics have potential to improve access to the primary care workforce. However, attention to supportive transition processes (such as clinical supervision) are required for the paramedic to successfully be integrated into the primary care team.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".