Potential risks to the National Health Service (NHS) of a Post-Brexit US Trade Deal
Bibliographic record
Abstract
At the very heart of the pro-Brexit narrative, currently supported by Eurosceptic politicians such as Boris Johnson, Michael Gove and David Davis, and Conservative think tanks including the Institute of Economic Affairs and Adam Smith’s Institute, is the belief that breaking European ties will enable Britain to reunite with the Anglosphere and notably its treasured ally, the United States. There are many indications that a post Brexit trade deal is in the pipeline with Donald Trump’s declaration in July 2019 that a “very substantial” trade deal was underway. Moreover, in late November 2019, Jeremy Corbyn contended that Conservatives were negotiating a secret trade deal containing clauses which would open the NHS up to American pharmaceutical companies. The central focus of this paper is thus to examine the likely consequences of a post-Brexit trade deal between the US and the UK and to consider to what extent it could undermine the UK’s ability to provide a free, universal public health service. In particular, it will examine empirical evidence on the impacts that FTAs have already had on access to medicine for countries which have been signatory to bilateral and plurilateral trade deals. Particular country contexts of price regimes for medication will be reviewed. It will also consider other FTA clauses such as public procurement and Investor State Dispute Settlement (ISDS) and their potential to disrupt national governments’ ability to protect public health service provision. Such evidence can then be used to hypothesise about the potential risks for Britain post Brexit.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".