Trade-offs, fairness, and funding for cancer drugs: key findings from a deliberative public engagement event in British Columbia, Canada
Bibliographic record
Abstract
Abstract Background Spending on cancer drugs has risen dramatically in recent years compared to other areas of health care, due in part to higher prices associated with newly approved drugs and increased demand for these drugs. Addressing this situation requires making difficult trade-offs between cost, harms, and ability to benefit when using public resources, making it important for policy makers to have input from many people affected by the issue, including citizens. Methods In September 2014, a deliberative public engagement event was conducted in Vancouver, British Columbia (BC), on the topic of priority setting and costly cancer drugs. The aim of the study was to gain citizensâ input on the topic and have them generate recommendations that could inform cancer drug funding decisions in BC. A market research company was engaged to recruit members of the BC general public to deliberate over two weekends (four days) on how best to allocate resources for expensive cancer treatments. Participants were stratified based on the 2006 census data for BC. Participants were asked to discuss disinvestment, intravenous versus oral chemotherapy delivery, and decision governance. All sessions were audio recorded and transcribed. Transcripts were analyzed using NVivo 11 software. Results Twenty-four individuals participated in the event and generated 30 recommendations. Participants accepted the principle of resource scarcity and the need of governments to make difficult trade-offs when allocating health-care resources. They supported the view that cost-benefit thresholds must be set for high-cost drugs. They also expected reasonable health benefits in return for large expenditures, and supported the view that some drugs do not merit funding. Participants also wanted drug funding decisions to be made in a non-partisan and transparent way. Conclusion The recommendations from the Vancouver deliberation can provide guidance to policy makers in BC and may be useful in challenging pricing by pharmaceutical companies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.661 | 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 teacher head, 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".