The Brilliance Project: trying to understand great performance in the health service
Bibliographic record
Abstract
The motivation behind having brilliance as a focus fora project in healthcare is about trying to understandit, and from that, trying to find ways of spreadingsuch understanding widely so that brilliance is morepervasive in health services. One member of the teamcommented that they had been referred to as running abrilliant team, and responded that if that was the case,how bad must the rest have been? Sometimes it is noteasy to see brilliance in what we do, let alone measureit, but on reflection we can remember times when theteamwork was just right, although we did not know it atthe time, but now see it as having been a special time.Hugh B MacLeod of the Canadian Patient Safety Institute(CPSI) has been pivotal in inspiring this work, and someof his input is outlined. This paper describes more ofthe background and motivation behind the project aswell as some of the potential ways in which the HealthManagement Research Alliance (HMRA) is investigatingand going about this project.
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 | 0.060 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.060 |
| Scholarly communication | 0.025 | 0.032 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.006 | 0.025 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".