A Role for Nurse Practitioners in the ICU: Ad ti f Chvoca ng or ange
Bibliographic record
Abstract
• Many regions of Canada and many areas in the , hospital already have nurse practitioners as part of their teams; for those areas that do not utilize Nurse Practitioners in the Intensive Care Unit, why now? • The dynamic state of critical care along with , increased demand for services often leaves the multidisciplinary team struggling to manage their patients while ensuring availability and excellence is provided to all . Why Nurse Practitioners? • The aging patient population, limited resources, and increasing complexity and acuity of patients requires that we look at new and imaginative ideas to ensure all p ti t h t mpr h i ppr pri ta en s ave access o co e ens ve, a o a e, universal care. • As highly specialized ICU nurses one way we can advocate for our patients, is by advocating for ourselves. Training ICU nurses to be acute care nurse i i (ACNP) i i i d ill h llpract t oners s s nnovat ve an w c a enge the existing boundaries of existing nursing practice and current health care delivery in many areas. • Nursing has recognized that the needs of acutely ill patients are not being adequately met, and that NPs have a scope of practice that if maximized can meet the needs of the patient, the medical system and the needs of nurses , . • (Becker, Kaplow, Muenzen, & Hartigan, 2006).
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".