The impact of nurse prescribing on health care delivery for patients with diabetes: a rapid review
Bibliographic record
Abstract
Introduction The global prevalence of diabetes is a pressing public health concern. Over 400 million individuals live with the effects of the disease, predominantly in low- and middle-income countries. In Aotearoa New Zealand (NZ), over 300 000 people have diabetes, resulting in a population rate of 43.1 per 1000. Enabling nurses to prescribe diabetes medications enhances accessibility and improves health outcomes for large sections of the population. Aim This rapid review was undertaken to investigate the influence of nurse prescribing on health care delivery for individuals with diabetes in NZ, Australia, the United Kingdom, and Canada, countries sharing comparable health care systems and multicultural backgrounds. Methods The review protocol was published on PROSPERO. In November 2022, a search was conducted across multiple databases to locate relevant literature and resources constrained to the last decade (from January 2012 to November 2022). Utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, data extraction was systematically structured, while rigorous appraisal processes upheld selection quality. Results Fifteen publications were identified as meeting predefined inclusion and exclusion criteria. The review of these articles revealed four main themes: the impact of nurse prescribing on clinical outcomes, levels of patient satisfaction, implications for health care service provisions, and identification of barriers and facilitators associated with nurse prescribing. Discussion This report identifies outcomes of nurse prescribing, concluding it provides a potential avenue for enhancing access to and alleviating the burden on health care systems.
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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".