Bibliographic record
Abstract
It appears new generic pricing policies implemented across Canada over the past couple of years are helping to slow the pace of growth in drug expenditures. Spending on drugs in Canada grew more slowly in 2012 than at any time in the previous 16 years, according to the Canadian Institute for Health Information (CIHI). In Drug Expenditure in Canada, 1985 to 2012, CIHI estimates that total drug spending reached $33 billion in 2012—an average of $947 per person. While that represents an increase of $3.3% from 2011 expenditures, it is historically a low rate of growth. The $33 billion includes both prescription and over-the-counter drugs. Prescribed drugs made up an estimated 84% of overall drug spending in 2012—a total of $28 billion. According to the report, public-sector spending on prescription drugs grew by 1.9% in 2012, the lowest growth rate since 1996. The estimated rate of growth in the private sector, which includes private insurers as well as households and individuals, was 4.1%—the lowest since 1994. New generic pricing policies in provincial drug programs are one factor in the spending slowdown, say the report’s authors. They also point to patent expirations of blockbuster brand-name drugs used to treat patients for high cholesterol and hypertension. Drugs continued to account for the second-highest share (15.9%) of health care spending, behind hospitals and ahead of spending on physicians. Over the past decade, spending in the other 2 categories has grown more rapidly than in the drug-spending area.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".