Nurse practitioner models of care in rural northern British Columbian emergency care settings
Bibliographic record
Abstract
Emergency room congestion and long wait times have become prevalent in emergency departments across Canada.Emergency care providers in the Northern Health Authority region of Northern British Columbia also struggle to provide quality emergency care in the face of challenges that impact access to timely emergency care.Nurse Practitioners are a new class of health care provider in British Columbia that have the skills and knowledge to provide care for many of the patients who present to Northern Health Authority emergency departments.The question posed in this project is as follows.Would a model of emergency care utilizing the NP role increase patient access and decrease wait times in British Columbia's Northern Health Authority emergency departments as compared to the current model of care?The focus of this project is a review of literature related to the role of Nurse Practitioners (NPs) in emergency department settings, as well as consideration of NP models of emergency care that would increase access and decrease wait times for quality emergency care in the Northern Health Authority.The results of the literature review support the addition of a NP model of care to emergency care setting in northern British Columbia.As Nurse Practitioners are deployed across British Columbia and the Northern Health Authority, continued assessment, analysis, planning, pilot project implementation, change management, evaluation and research related to NP roles in emergency care settings are pivotal to the successful implementation of NP models of emergency care in the region.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".