People will support what they help to create: clinical governance large group work
Bibliographic record
Abstract
The NHS Clinical Governance Support Team (CGST) has completed a pilot “protected-time programme”, supported by a small team of national facilitators and delivered locally in 19 NHS pilot sites across England. The programme worked on the premise that health professionals can successfully lead service developments when given time and space to do so. The paper describes the methodology behind this initiative, how local events were organised to “get the whole system into the room” and what was learned by applying tried and tested methodologies such as accelerated service improvement. Some of the changes being implemented in participating NHS Trusts are presented in brief, along with a more detailed case study of work undertaken at Royal Cornwall Hospitals Trust. Having completed the pilot programme, the team is supporting other CGST activities and applying the learning from working with large groups to improve locally delivered care in NHS organisations.
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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 | 0.047 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".