Overview of the uptake and implementation of non-medical prescribing in Wales: a national survey
Bibliographic record
Abstract
OBJECTIVES: To identify (1) the non-medical healthcare professionals in Wales qualified to prescribe medicines (including job title, employer, where the prescribing qualification is used, care setting and service provided); (2) the mode of prescribing used by these healthcare professionals, the frequency with which medicines are prescribed and the different ways in which the prescribing qualification is used; and (3) the safety and clinical governance systems within which these healthcare professionals practise. DESIGN: National questionnaire survey. SETTING: All three National Health Service (NHS) Trusts and seven Health Boards (HB) in Wales. PARTICIPANTS: Non-medical prescribers. RESULTS: 379 (63%) participants responded to the survey. Most of these prescribers (41.1%) were specialist nurses who work in a variety of healthcare settings (primarily in secondary care) within each HB/NHS Trust, and regularly use independent prescribing to prescribe for a broad range of conditions. Nearly a quarter of the sample (22%) reported that prior to undertaking the prescribing programme, they had completed master's level specialist training and 65.5% had 5 years qualified experience. Over half (55.8%) reported that there were plans to increase non-medical prescriber numbers within the team in which they worked. Only 7.1% reported they did not prescribe and the median number of items prescribed per week was between 21 and 30. Nearly all (87.8%) of the sample reported that they perceived prescribing to have ensured better use of their skills and 91.5% indicated that they believed it had improved the quality of care they were able to provide. CONCLUSION: Non-medical prescribing has been implemented across the whole of Wales; however, its uptake within HBs and NHS Trusts has been inconsistent, and it has not been considered across all services, particularly those in primary care. Opportunities therefore exist to share learning across organisations.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".