Public perspectives on disinvestments in drug funding: results from a Canadian deliberative public engagement event on cancer drugs
Bibliographic record
Abstract
BACKGROUND: Decisions relating to the funding of new drugs are becoming increasingly challenging due to a combination of aging populations, rapidly increasing list prices, and greater numbers of drug-indication pairs being brought to market. This is especially true in cancer, where rapid list price inflation is coupled with steeply rising numbers of incident cancer cases. Within a publicly funded health care system, there is increasing recognition that resource allocation decisions should consider the reassessment of, and potential disinvestment from, currently funded interventions alongside new investments. Public input into the decision-making process can help legitimize the outcomes and ensure priority-setting processes are aligned with public priorities. METHODS: In September 2014, a public deliberation event was held in Vancouver, Canada, to obtain public input on the topic of cancer drug funding. Twenty-four members of the general public were tasked with making collective recommendations for policy-makers about the principles that should guide funding decisions for cancer drugs in the province of British Columbia. Deliberative questions and decision aids were used to elicit individuals' willingness to make trade-offs between expenditures and health outcomes. RESULTS: Participants discussed the implications of disinvestment decisions from cancer drugs in terms of its impact on patient choice, fairness and quality of life. Their discussions indicate that in order for a decision to disinvest from currently-funded cancer drugs to be acceptable, it must align with three main principles: the decision must be accompanied by significant gains, described both in terms of cost savings and opportunities to re-invest elsewhere in the health care system; those who are currently prescribed a cancer drug should be allowed to continue their course of treatment (referred to as a continuance clause, or "grandfathering" approach); and it must consider how access to care for specialized populations is impacted. CONCLUSIONS: The results from this deliberation event provide insight into what is acceptable to British Columbians with respect to disinvestment decisions for cancer drugs. These recommendations can be considered within wider health system decision-making frameworks for funding decisions relating to all drugs, as well as for cancer drugs.
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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 | 0.078 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.054 | 0.018 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.014 |
| 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".