“There is <i>always</i> a better way”: Managing uncertainty in decision making about new cancer drugs in Canada
Bibliographic record
Abstract
Policy decisions about the approval and funding of new cancer drugs must often be made in an environment of complex uncertainty about clinical and cost-effectiveness data. The focus of this article is on the results from qualitative interviews with senior officials (n = 16) who make decisions about or influence cancer drug policy in various organizations in the Canadian cancer control system. Most participants identified the use of a limited number of informal approaches to address uncertainty, such as grounding decisions in evidence and advice from expert groups. People tended to focus on evidence informed decisions including price negotiations, the ability to implement policy changes, and stakeholder values. Lessons from the Canadian context related to continuing efforts to build a public culture of understanding into how policy decisions like cancer drug funding are made may result in greater acceptance and increased confidence in health policy decision-making processes across multiple sectors internationally.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.037 | 0.025 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| 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".