An evidence based proposal for developing an advanced nurse practitioner led ambulatory emergency care service
Bibliographic record
Abstract
The purpose of this dissertation is to support an evidence-based change in practice in the way that the Ambulatory Emergency Care (AEC) Services, based within a South West Scotland District General Hospital are developed to promote an Advanced Nurse Practitioner (ANP) led service. The introduction of a new ANP led service should provide a more efficient service and reduce admission rates than the traditional service (Simcox, 2013). It will also ensure that the local NHS service meets the local and national targets to provide integrated safe and competent care for patients attending the AEC. Rising medical admission rates (Royal College of Physicians, 2012), the pressures of bed capacity, patient flow and the effect this can have on patient care and experience, the ageing population, particularly in the local region (Local Board XXX, 2015/16), increased patient complexities and co-morbidities brings challenges and opportunities to the way in which we deliver healthcare to the acute medical admission patient (Edwards et al, 2013). A report by the Kings Fund (2016) found that in the first quarter of the 2016/17 period medical admission rates had increased compared to the previous quarter and that increased demand for services was placing the health service under huge strain as more than 90% of beds were occupied, well above the level that is considered to be safe. The National Audit Office (2013) suggest that the factors that contribute to the rise in admission rates are that Emergency Departments (ED) and admission to hospital are seen as the default route for emergency and urgent care and that the NHS is slow to develop alternative routes than admission, this alongside the four hour target in ED and the inability to provide a period of observation for patients that could be discharged same day has resulted in an increase in short-stay emergency admissions.
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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 | 0.148 | 0.228 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.035 | 0.031 |
| Insufficient payload (model declined to judge) | 0.015 | 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".