Regulation of Free-Standing Health Facilities: An Entrée for Privatization and For-Profit Delivery in Health Care
Bibliographic record
Abstract
Though it often appears beleaguered and bedraggled in newspaper and television reports on the state of its health, the Canadian medicare system continues to enjoy strong public support. Despite some serious areas of unmet need and undercapacity, as well as decreasing public confidence that the health care system will be able to meet their needs in the future, people expect deficiencies to be addressed in a way that maintains both the publicly funded system and access to it. Politicians perceived as attacking it, then, do so at their peril. Nonetheless, that is what is occurring. Acting not only out of a concern to control costs, but also from ideological commitments to a sharply limited role for the state, several provinces have adopted policy agendas that will increase privatization in health care. Because of the political risks in doing so, however, shifts in that direction are being accomplished obliquely, either without acknowledging the end result or by focussing attention on other, more palatable consequences. A number of these shifts are supported and indeed, made possible by changes in legislation and regulations. While much of this activity may not directly privatize the funding, organization and delivery of health care (indeed, it may be promoted as improving the publicly funded system), its effect is to do so, or to set the stage for further privatization.
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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 | 0.014 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| 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".