Financing, Providing and Monitoring the Health Care System through Public-Private Partnerships in Quebec: A Serious Option to Consider or a Pact with the Devil
Bibliographic record
Abstract
For years, public-private partnerships (P3s) have been seen as a key element of New Public Management (NPM) and of the debates surrounding the redefinition of the role of the State. They have recently gained prominence as a term used to describe a business relationship between government organizations and private enterprises which put together their resources in order to achieve certain goals or sets of goals. In the actual context of public financial crises and national debt all over the world, governments are being forced to reallocate scarce resources for the utmost effectiveness. In order to do so, they are trying to find a better way of managing the public sector. According to many authors, classical Keynesian approaches based on state intervention are no longer effective, if only with respect to difficulties faced by the public sector. New partnerships are needed. A common argument in favour of these partnerships is that operations and services, traditionally the responsibility of government, can be run more efficiently, in a more entrepreneurial fashion, and with a less cumbersome bureaucracy and waste, by delegating certain public services to private enterprises. But the P3 model of providing health care infrastructure and services raises many fears in the health sector. Given a stubborn rhetoric and the hazardous slipping of public services towards radical privatization as practiced in the United States, certain authors claim that, you can't sup with the devil, even with a long spoon. According to others, great mirages and irrefragable alternatives, P3s are still the subject of sharp debates and criticism with ideological flavour and dogmatic connotations. In this paper, we propose to go beyond these paradigmatic considerations in order to examine public-private partnerships for what they truly are, with their strengths and their weaknesses. As P3s are not carried out without obstacles, as they can constitute a lifesaver for a public sector facing difficulties, assuming that certain guarantees regarding the health care are obtained and that conditions pertaining to their design, their implementation and their management are filled.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".