Enhancing Critical Care Transport in Northern, Rural, and Remote Ontario: The Role of Nurse Practitioners
Bibliographic record
Abstract
Aim: To examine the challenges and potential solutions regarding delays in interfacility transport of critically ill adult patients in northern, rural, and remote regions of Ontario, focusing on integrating Nurse Practitioners (NPs) into critical care teams. Background: Due to geographic barriers, critical care transport delays are problematic in northern, rural, and remote Ontario. Timely transfers to lead trauma centers are crucial for patient outcomes. However, delays occur due to improper triage, physician shortages, and lengthy decision-to-transfer times. NPs’ role in addressing healthcare challenges in these areas, specifically within critical care, could be significant. Method: An integrative review analyzed NPs' impact on critical care coordination in underserved regions of Ontario. A thorough search across five databases yielded 16 relevant studies meeting specified criteria that were then assessed for quality using the Mixed Methods Appraisal Tool. Findings: Three key themes were identified, including the role of NPs in critical care teams and NP-led models of care, telehealth utilization by NPs in underserved communities, and successful NP integration models. Conclusions: NPs effectively deliver primary and emergency care via telehealth in rural areas despite the lack of a formal funding model. Advocating for NPs ability to work within their full scope of practice with sustainable funding while exploring innovative roles like Remote Critical Care Nurse Practitioners, could optimize care delivery. Investing in NP-led initiatives promises to enhance healthcare access, quality, and equity in northern, rural, and remote Ontario.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".