Assessing policy transfer from the United States to the British National Health Service
Bibliographic record
Abstract
Much has been written about the claim that the British National Health Service (NHS) is becoming more like the US health care system, something a number of commentators view as a form of "Americanization".Yet, that term is imprecise and unhelpful for rigorous analysis of what has, and has not, happened.This paper uses the lens of policy transfer to explore this issue, which provides a sharper insight into policy development.The paper examines the relevance of the Dolowitz and Marsh framework for the study of policy transfer from the US to the British NHS from 1979 onwards.In terms of the framework's main research questions, the discussion of the potential US influence on the NHS case stresses the role of policy entrepreneurs in policy transfer.In terms of policy success, however, commentators suggest a mix of uninformed, incomplete, or inappropriate transfer.We conclude that Dolowitz and Marsh do provide a useful framework that asks relevant questions about policy transfer, which provides a more nuanced account of policy transfer from the US to the NHS than the crude term "Americanization".
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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 | 0.037 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".