Inter-jurisdictional cooperation on pharmaceutical product listing agreements: views from Canadian provinces
Bibliographic record
Abstract
BACKGROUND: Confidential product listing agreements (PLAs) negotiated between pharmaceutical manufacturers and individual health care payers may contribute to unwanted price disparities, high administrative costs, and unequal bargaining power within and across jurisdictions. In the context of Canada's decentralized health system, we aimed to document provincial policy makers' perceptions about collaborative PLA negotiations. METHODS: We conducted semi-structured telephone interviews with a senior policy maker from nine of the ten Canadian provinces. We conducted a thematic analysis of interview transcripts to identify benefits, drawbacks, and barriers to routine collaboration on PLA negotiations. RESULTS: Canadian policy makers expressed support for joint negotiations of PLAs in principle, citing benefits of increased bargaining power and reduced inter-jurisdictional inequities in drug prices and formulary listings. However, established policy institutions and the politics of individual jurisdictional authority are formidable barriers to routine PLA collaboration. Achieving commitment to a joint process may be difficult to sustain among heterogeneous and autonomous partners. CONCLUSIONS: Though collaboration on PLA negotiation is an extension of collaboration on health technology assessment, it is a very significant next step that requires harmonization of the outcomes of decision-making processes. Views of policy makers in Canada suggest that sustaining routine collaborations on PLA negotiations may be difficult unless participating jurisdictions have similar policy institutions, capacities to implement coverage decisions, and local political priorities.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".