Mid‐level providers and emergency care: Let's not lose the force
Bibliographic record
Abstract
The progressive rise of ED visits globally, and insufficient numbers of emergency physicians, has resulted in the use of mid-level providers as adjuncts for the provision of emergency care, especially in the US and Canada. Military medics, midwives, aeromedical paramedics, EMT-Ps, flight nurses, forensic nurses, sexual assault nurse examiner nurses--are some examples of well-established mid-level provider professionals who achieve their clinical credentials through accredited training programmes and formal certification. In emergency medicine, however, mid-level providers are trained for general care, and typically acquire emergency medicine skills through on-the-job experience. There are very few training programmes for NPs and PAs in emergency care. The manpower gap for physicians in general, and emergency physicians specifically, will not be eliminated in the reasonable future. Mid-level providers--ENTs, paramedics, NPs, PAs--are an excellent addition to the emergency medicine workforce. However, the specialty of emergency medicine developed because specific and focused training was needed for physicians to practice safe and qualify emergency care. This same principle applies to mid-level providers. Emergency Medicine needs to develop a vision and a plan to train emergency medicine specialist NPs and PAs, and explore other innovations to expand our emergency care workforce.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".