Bibliographic record
Abstract
Provincial health spending has grown faster on average than GDP for the last 37 years. Trends show that health spending will consume 50% of total available revenues (including federal transfers) in 6 of 10 provinces by 2017, up from roughly 25% in 1974. Some researchers blame unsustainable growth in government health spending on the cost of prescription drugs, particularly patented medicines. The evidence suggests otherwise. Prescription drugs account for a small percentage (9%) of government health spending; and patented prescription drugs are an even smaller percentage (5.2%). Excluding prescription drugs, all other non-drug categories of health expenditures (hospitals, professionals, etc.) are growing at an unsustainable pace, while accounting for 91% of government spending on health. There is no observable statistical link between the rising share of the health budget spent on drugs and variation in the growth rates in government health spending. Inflation-adjusted, post-market prices for patented drugs in Canada have been declining for 21 years, and introductory prices for patented drugs are at or below international prices.The real cause of unsustainable growth in health spending is that government socializes too much of the private consumption costs of healthcare. Provinces subsidize 100% of the cost of medical goods and services through a redistributive, tax-funded, single-payer, government-run, insurance monopoly. Coverage is universal for hospital and physician services, but extends to drugs for only one-third of the population. Consumers are disconnected from the costs of the healthcare they personally use. As a result, the system lacks the normal economic incentives that would produce a sustainable balance between the demand for and supply of medical goods and services. Instead, governments constrain costs through central budget rationing, which creates intractable shortages because, while private insurance could cover unmet consumer demands for healthcare, governments effectively prohibit private payment for hospital and physician services.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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".