An evaluation of nurse prescribing. Part 1: a literature review
Bibliographic record
Abstract
BACKGROUND: Prescriptive authority for nurses was first introduced by America in 1969, followed later by the UK, Canada, New Zealand, Australia and Sweden. A review of research conducted internationally was performed to inform the development of prescribing policies and practice and to guide future research. AIMS: This article, the first of a 2-part literature review, aims to provide a summary of the research conducted in relation to nurse prescribing and confidence in prescribing, the impact of prescribing on relationships, and education for prescribing. METHOD: A number of electronic databases were searched in March 2009 and 155 results were retrieved. Forty-four studies satisfied the criteria for inclusion. Twenty-two of these studies are included in this part of the review. FINDINGS: Seventeen studies were UK based, with USA, Australia and Sweden represented by a minority of studies. Seven studies addressed nurse prescribers' confidence, while eight studies focused on the impact nurse prescribing has on interprofessional relationships. The final seven studies addresses the knowledge base and educational preparation of nurses for the prescribing role. CONCLUSION: This review has demonstrated the diversity of research conducted in the area of confidence in prescribing, interprofessional relationships and education. It has identified areas which require further investigation, which will inform the future development of nurse and midwife prescribing.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".