Research to Action: Nurse Practitioners in the Emergency Department, Emergency Department Transition Clinic and Intravenous Therapy Clinic at Strathcona Community Hospital
Bibliographic record
Abstract
This article is about nurse practitioners effectiveness working in the Strathcona emergency department (ED), as well as the efficacy of two NP led clinics that run parallel to the ED. Prior to opening Strathcona Community Hospital in 2014, site leadership were tasked with developing an innovative care model with the aim of improved patient safety and quality of care delivered, incorporating a nurse practitioner (NP) model. There are NPs in three areas at Strathcona Community Hospital. NPs work directly in the ED, assessing and treating patient autonomously and with emergency physician collaboration. They also complete diagnostic and microbiology review and perform triage liaison nurse practitioner (TLNP) duties. There is also an NP led Emergency Department Transition Clinic (EDT) for urgent or emergent follow up patients from the ED. Lastly, the NP led Intravenous Therapy Clinic was developed to see patients previously attended through the ED for IV antibiotic and other IV non-antibiotic treatments, while supporting increased community access to IV treatments. Evaluation of the 3 areas was completed using qualitative and quantitative methods over the period of 2015-2018. Statistical analysis was completed by the Alberta Health Services workforce team. Outcomes included reduced patient return visits, decreased wait times and patients leaving without treatment (LWOT), and high patient satisfaction. Other results included improved staff satisfaction, facilitation of continuity of care and avoiding unnecessary ED visits.
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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".