Quality Improvement: Lessons from the English National Health Services
Bibliographic record
Abstract
Based on our own experiences leading healthcare improvement in the English National Health Service (NHS), we identify seven themes that connect with the story of front-line ownership (FLO): Create investors not buyers of change - "buy-in" is too late in the change process; We need to combine both technical and cultural aspects of change - go slow to go fast and make sure that we pay attention to the human dimensions of change; Build strong ties AND weak ties - reach out to your usual suspects AND find your unusual suspects and unlikely allies; If we want innovation, we need to create psychological safety - the conditions of trust and support that make people feel safe to try new things that might fail; Build energy for change for the long haul, right from the start of your change efforts - go beyond the typical "intellectual" energy and build "social" and "spiritual" energy that create strong foundations for change; Challenge "the myth of the disinterested" - everyone is passionate about something; The leader as coach and team member - be the leadership role model that enables change.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".