Cost-Control Mechanisms in Canadian Private Drug Plans
Bibliographic record
Abstract
Approximately 68% of Canadians receive prescription drug coverage through an employer-sponsored private plan. However, we have very limited data on the structure of these plans. This study aims to identify and describe the use of cost-control mechanisms in private drug plans in Canada and describe what private coverage looks like for the average Canadian. Using 2010 data from over 113,000 different private drug plans, provided by Applied Management Consultants, we determined the overall use of key cost-control measures, and the cost-control tools that appear to be gaining currency compared to a report on benefits coverage in 1998. We found that the use of common cost-control measures is relatively low among Canadian private benefits programs. Co-insurance is much more common in private coverage plans than co-payments. Deductibles are uncommon in Canada and, when in place, are very small. The use of annual and lifetime maximums is increasing. Canadian private benefits programs use few cost-control measures to respond to increasing costs, particularly in comparison to their public counterparts. These results suggest there are ample opportunities for greater efficiency in private sector drug coverage plans.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".