A Deductive Analysis of Public Private Partnerships for Health Technology
Bibliographic record
Abstract
In several countries, consulting firms, think thanks and even government agencies spend a considerable amount of energy trying to expand the scope of public private partnerships (PPPs). Initially confined to the construction and maintenance of public infrastructures, PPPs are currently discussed and experimented with in sectors as diverse as health care provision, crime reduction, immigrants’ integration and even the organization of elections. This paper discusses the way PPPs are likely to transform the adoption of novel medical technology in countries where health care is publicly funded. I believe, however, that several ideas presented here can be useful to consider in applying PPPs to all sectors of state intervention relying on expensive technologies. In the first section, I begin by presenting the economic understanding upon which PPPs rest. I then present the simple and uncontroversial assumption that, in democratic countries, PPPs are negotiated by politicians. Withholding the rationality assumption upon which economic theory rests, I argue that rational politicians are unlikely to prefer a PPP contract appealing to a private partner, unless politicians accept occasional renegotiation of given clauses of PPP contracts. Where this occurs, however, the alleged economic efficiency of PPPs is seriously undermined. In the second section, I present a series of reasons to contest economic theory’s treatment of health technology choices as economic choices. These reasons, I suggest, made significant contributions to health technology assessment and purchase reforms. The economic reasoning behind PPPs, I conclude, poses a serious threat to these reforms.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.001 |
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".