Tarnished Yet Tenacious: Examining the Track Record and Future of Public-Private Partnership Hospitals in Canada
Bibliographic record
Abstract
Public—private partnerships (P3s) with the for-profit private sector first emerged in Canada in the mid-1990s and have been steadily growing in popularity ever since. More recently, proliferation since the mid-2000s has been sustained largely through infrastructure projects developed within provincial health sectors. By 2011, P3 hospitals accounted for half to three-quarters of all P3 projects in British Columbia (BC) and Ontario respectively, the Canadian provinces most enthusiastic for P3s (see Partnerships BC n.d.; Infrastructure Ontario n.d.). 1 With large hospital re/development in particular, P3s are now the principal way in which these infrastructure projects and their accompanying support services are delivered. While the rise of P3s is a global phenomenon, which is in no way unique to Canada, specificity when evaluating this policy matters. By focusing on Canadian P3 hospitals, we are able to uncover how global neoliberal processes operate at the ground level (in the form of particular P3 projects) and how this unfolds within a specific sector (health care). In so doing it becomes evident that location-and sector-specific conditions shape the implementation of larger neoliberal forces. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".