Canadian policy makers’ views on pharmaceutical reimbursement contracts involving confidential discounts from drug manufacturers
Bibliographic record
Abstract
Pharmaceutical policy makers are increasingly negotiating reimbursement contracts that include confidential price terms that may be affected by drug utilization volumes, patterns, or outcomes. Though such contracts may offer a variety of benefits, including the ability to tie payment to the actual performance of a product, they may also create potential policy challenges. Through telephone interviews about this type of contract, we studied the views of officials in nine of ten Canadian provinces. Use of reimbursement contracts involving confidential discounts is new in Canada and ideas about power and equity emerged as cross-cutting themes in our interviews. Though confidential rebates can lower prices and thereby increase coverage of new medicines, several policy makers felt they had little power in the decision to negotiate rebates. Study participants explained that the recent rise in the use of rebates had been driven by manufacturers' pricing tactics and precedent set by other jurisdictions. Several policy makers expressed concerns that confidential rebates could result in inter-jurisdictional inequities in drug pricing and coverage. Policy makers also noted un-insured and under-insured patients must pay inflated "list prices" even if rebates are negotiated by drug plans. The establishment of policies for disciplined negotiations, inter-jurisdictional cooperation, and provision of drug coverage for all citizens are potential solutions to the challenges created by this new pharmaceutical pricing paradigm.
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 | 0.035 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.048 | 0.023 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".