Long-term conditions and the National Diabetes Audit
Bibliographic record
Abstract
Clinical pharmacists in general practiceThe recent article by Williams et al estimated that one clinical pharmacist post in Westbourne Medical Centre saves a GP 80 hours a month. 1 Researchers in Dudley determined that 769.6 GP hours were saved by 5.4 full-time equivalent pharmacists over 4 months between September and December 2015. 2 This equates to one post saving a GP 35.6 hours per month. 2 We estimated the potential time saved for GPs by tasks being undertaken by part-time pharmacists in three general practices in Canberra, Australia, at 23% from May to December 2017.Assuming that a full-time pharmacist works 37.5 hours per week, our data suggest that 37.4 hours per month of GP time may be saved by one full-time pharmacist.This comparison suggests that differences in GP hours saved may depend on the different activities undertaken by the pharmacists, their clinical experience, or the different methods of coding activities as a GP task.Making a cost-effectiveness case for pharmacists in general practice is complex.Using GP hours saved alone underestimates the health economic value of pharmacists in general practice.Other contributions that can be considered include hospital admission avoidance due to safer prescribing, 1,3 reduced drug costs, 2 involvement in government payments for quality or specific services, 1,4 and improved clinical outcome measures.5 We agree that using GP hours saved implies that pharmacists are 'cheap doctors or expensive nurses' 1 but feel that using GP hours saved will be a necessary component of cost-effectiveness calculations until pharmacists are universally accepted as essential to the general practice team.
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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".