Bibliographic record
Abstract
The government set a target in 2002 to have 10000 nurses qualified as supplementary and extended formulary prescribers by the end of 2004. In retrospect, this was optimistic. Despite the rhetoric, and the exhortations from ministers and managers, the uptake of training for nurse prescribing has been slow. Recent figures from the Department of Health (DH) suggest that there are currently 1807 nurses registered to prescribe from the Extended Formulary, and 1087 nurses registered as supplementary prescribers (Mullally, 2004). Of the 1087 supplementary prescribers, only 110 are mental health nurses and a quarter of those are employed by one trust. However, such modest progress is not perhaps surprising considering the scale of the innovation and the fact that nurses often combine a desire to be progressive with a cautious attitude.
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 | 0.021 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.071 | 0.076 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".