Do reimbursement recommendation processes used by government drug plans in Canada adhere to good governance principles?
Bibliographic record
Abstract
In democratic societies, good governance is the key to assuring the confidence of stakeholders and other citizens in how governments and organizations interact with and relate to them and how decisions are taken. Although defining good governance can be debatable, the United Nations Development Program (UNDP) set of principles is commonly used. The reimbursement recommendation processes of the Canadian Agency for Drugs and Technologies in Health (CADTH), which carries out assessments for all public drug plans outside Quebec, are examined in the light of the UNDP governance principles and compared with the National Institute for Health and Care Excellence system in England. The adherence of CADTH's processes to the principles of accountability, transparency, participatory, equity, responsiveness and consensus is poor, especially when compared with the English system, due in part to CADTH's lack of genuine independence. CADTH's overriding responsibility is toward the governments that "own," fund and manage it, while the agency's status as a not-for-profit corporation under federal law protects it from standard government forms of accountability. The recent integration of CADTH's reimbursement recommendation processes with the provincial public drug plans' collective system for price negotiation with pharmaceutical companies reinforces CADTH's role as a nonindependent partner in the pursuit of governments' cost-containment objectives, which should not be part of its function. Canadians need a national organization for evaluating drugs for reimbursement in the public interest that fully embraces the principles of good governance - one that is publicly accountable, transparent and fair and includes all stakeholders throughout its processes.
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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 | 0.044 | 0.174 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.014 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".