Knowledge and Behaviour for a Sustainable Improvement Culture
Bibliographic record
Abstract
Wait limits have improved UK healthcare access, and Ontario's Wait Time Strategy bears a remarkable resemblance. There appears to be an implicit assumption that capacity and efficiency factors are the main causes of waits. The improvement mechanism is driven by performance measurement that reports wait time outcomes. Our experience makes us conclude that Ontario's plans contain risks. Superficially, the UK approach has been successful with dramatic wait time reductions but has incurred tremendous financial cost and patients not always benefiting. Reasons for partial success are not understanding the cause of waiting, with inappropriate"improvements"; and often encouraging unintended behaviours, with poor stakeholder management. Those sustaining their approach have significantly better performance and timely service without excess cost, but their approach has not seen a wide enough audience for acceptance and adoption. At the top, there is almost bewilderment about why others struggle with wait time targets. For an effective program it is essential to understand the system and have consistency between the measurement system and engendered behaviour, the root causes of waits and solutions, the management style and improvement culture, the reward system and good clinical practice.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.036 | 0.032 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".