Bibliographic record
Abstract
BACKGROUND: Canadians spent almost dollars 15 billion, over dollars 460 per capita, on prescription drugs in 2002, yet there is little published evidence regarding the nature and causes of these expenditures. OBJECTIVE: : The objective of this study was to describe the nature and determinants of prescription drug expenditures in Canada during a recent period of rapid expenditure inflation, 1998 to 2002. RESEARCH DESIGN: : Trends in overall expenditures and investment in specific therapeutic categories are decomposed using nonstochastic index-theoretical methods. MEASURES: Changes in per capita expenditures on oral solid prescription drugs are attributed to the cost-impact of changes in the 6 determinants that fall into 3 broad categories: volume effects, price effects, and therapeutic choices. RESULTS: A majority of spending was concentrated among only 5 therapeutic classes. After adjusting for generic drug use, prices for unchanged drugs declined over the period of analysis. Increased utilization of prescription drugs explained over half of the overall increase in per capita spending. Changes in therapeutic choice also contributed to cost increases. CONCLUSIONS: Findings suggest that the combined affect of federal price regulations, provincial price freezes, and generic substitution policies are controlling price-related determinants of drug spending in Canada. However, the cost-impact of increased drug utilization and changes in therapeutic choices illustrate the potential pitfalls of cost-management strategies that focus primarily on prices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".