Update from RCP Quality Improvement: QI, what do we need to learn?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quality improvement activities are now an established part of the training of postgraduate doctors in the UK, largely by being involved with or leading quality improvement projects. Learning activities should enable the development of professional capabilities that are outlined by the General Medical Council (GMC).1 However, the more detailed knowledge, skills and practice that need to be learned through this had not been clearly described. It is now widely accepted that quality improvement includes both technical and behavioural elements, and that learning these through practical experience as well as source materials is necessary. The Academy of Medical Royal Colleges (AoMRC) report Quality Improvement – training for better outcomes published in 2016 started to outline knowledge, skills, values and behaviours that would be required within a quality improvement curriculum at different stages of medical careers and recommended that royal colleges should develop these further.2 Work over the last 2 years has continued, with the medical royal colleges quality improvement leads …
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it