The Brilliance Project: trying to understand great performance in the health service
Bibliographic record
Abstract
The motivation behind having brilliance as a focus for\na project in healthcare is about trying to understand\nit, and from that, trying to find ways of spreading\nsuch understanding widely so that brilliance is more\npervasive in health services. One member of the team\ncommented that they had been referred to as running a\nbrilliant team, and responded 'that if that was the case,\nhow bad must the rest have been?' Sometimes it is not\neasy to see brilliance in what we do, let alone measure\nit, but on reflection we can remember times when the\nteamwork was just right, although we did not know it at\nthe time, but now see it as having been a special time.\nHugh B MacLeod of the Canadian Patient Safety Institute\n(CPSI) has been pivotal in inspiring this work, and some\nof his input is outlined. This paper describes more of\nthe background and motivation behind the project as\nwell as some of the potential ways in which the Health\nManagement Research Alliance (HMRA) is investigating\nand going about this project.\nAbbreviations: AI – Appreciative Inquiry; B – Based;\nCPSI – Canadian Patient Safety Institute; E-BM – Evidencebased\nMedicine; GBS – Griffith Business School;\nHMRA – Health Management Research Alliance;\nHRT – Health Results Team; JHHS – Johns Hopkins Health\nSystem; NHS– National Health Service; PGPI – Press\nGaney Priority Index; QI – Quality Improvement;\nSHAPE – Society for Health Administration Programs\nin Education; UNE – University of New England;\nUTS – University of Technology Sydney.\nKey Words: brilliance; brilliant performance; quality\nimprovement; patient safety.
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How this classification was reachedexpand
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 | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".