Solo emergency care by a physician assistant versus an ambulance nurse: a cross-sectional document study
Bibliographic record
Abstract
BACKGROUND: This study compares the assessment, treatment, referral, and follow up contact with the dispatch centre of emergency patients treated by two types of solo emergency care providers in ambulance emergency medical services (EMS) in the Netherlands: the physician assistant (PA), educated in the medical domain, and the ambulance registered nurse (RN), educated in the nursing domain. The hypothesis of this study was that there is no difference in outcome of care between the patients of PAs and RNs. METHODS: In a cross-sectional document study in two EMS regions we included 991 patients, treated by two PAs (n = 493) and 23 RNs (n = 498). The inclusion period was October 2010-December 2012 for region 1 and January 2013-March 2014 for region 2. Emergency care data were drawn from predefined and free text fields in the electronic patient records. Data were analysed using descriptive statistics. We used χ (2) and Mann-Whitney U tests to analyse for differences in outcome of care. Statistical significance was assumed at a level of P <0.05. RESULTS: Patients treated by PAs and RNs were similar with respect to patient characteristics. In general, diagnostic measurements according to the national EMS standard were applied by RNs and by PAs. In line with the medical education, PAs used a medical diagnostic approach (16 %, n = 77) and a systematic physical exam of organ tract systems (31 %, n = 155). PAs and RNs provided similar interventions. Additionally, PAs consulted more often other medical specialists (33 %) than RNs (17 %) (χ (2) = 35.5, P <0.0001). PAs referred less patients to the general practitioner or emergency department (50 %) compared to RNs (73 %) (χ (2) = 52.9, P <0.0001). Patient follow up contact with the dispatch centre within 72 h after completion of the emergency care on scene showed no variation between PAs (5 %) and RNs (4 %). CONCLUSIONS: In line with their medical education, PAs seemed to operate from a more general medical perspective. They used a medical diagnostic approach, consulted more medical specialists, and referred significantly less patients to other health care professionals compared to RNs. While the patients of the PAs did not contact the dispatch centre more often afterwards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".