What Can We Learn from the UK’s “Natural Experiments” of the Benefits of Regions?
Bibliographic record
Abstract
Marchildon highlights the lack of evidence on policies of regionalization in Canada: with regionalization being in favour in the 2000s followed by disillusion and the abolition of regions by some provincial governments. This paper looks at evidence from the UK's single-payer system of the impacts of regions on the performance of the delivery of healthcare. In England, regions were an important part of the hierarchical structure of the National Health Service (NHS) from its beginning, in 1948, to the introduction of provider competition, in the 1990s. Since then, in England, governments have understood that the NHS cannot be run from Whitehall and have tried to replace hierarchical control by provider competition. The consequence was that regions in England were subjected to frequent reorganizations from the mid-1990s with their abolition being announced in 2010. In contrast, the devolved countries of the UK have always been organized as "regions" in the form of their historic national boundaries. This paper argues that changes in the NHS in the UK in the 1990s and 2000s offer three "natural experiments," in terms of funding, organization and models of governance, that give evidence of the impacts of stable regions in the UK. It also considers the lessons of this evidence for Canada.
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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 | 0.026 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.057 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 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".