Bibliographic record
Abstract
system for appointments which we had for over 10 years but were not performing well on 'access' because of a high number of patients who did not attend (DNA).We often had over 100 DNAs per month so many appointments were being wasted.I noticed that another local practice which permitted advance booking only 2 days ahead scored better in the 'access' survey than our practice.Another local practice was piloting a same day booking system with no appointments booked in advance, from June 2013.We calculated that we had nearly the correct number of GP and nurse appointments per 1000 patients, per week.The Local Medical Committee had advised 100 appointments per 1000 patients per week.We are an average size practice of 6400 patients.An audit of the DNAs in April 2013 showed that 80% of DNAs had booked more that 7 days previously, so we changed to a 1-week advance booking system from 1 July 2013 with 50% of appointments bookable in advance and 50% available on the day, for GP appointments but not nurse appointments.The 'same day' appointments were unblocked on the day at 8 am each morning to prevent them being booked online.A repeat audit of DNAs in October 2013 showed that 75% of patients who DNA had booked more than 3 days ahead so we have just changed to a similar 3-day booking system from Monday 9 December 2013.This has reduced our DNAs and reduced stress within the practice.Other practices in the UK may wish to consider these ideas.I have concluded that a 2-3 day advance booking system is the right one for our practice and will probably be optimal for most practices.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.143 | 0.061 |
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".