Update from RCP Quality Improvement: QI, what do we need to learn?
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 …
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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 | 0.014 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".