Engaging the Canadian public on reimbursement decision-making for drugs for rare diseases: a national online survey
Bibliographic record
Abstract
BACKGROUND: Funding of drugs for rare diseases (DRDs) requires decisions that balance fairness for all individuals within the healthcare system with compassion for affected individuals. Our study objective was to conduct a national online survey to determine the Canadian public's perspective, including regional variations, associated with DRD decision-making. METHODS: The survey collected responses from 1631 Canadians. Respondents were asked to rank at least three and up to five DRD decision-making priorities, out of a total of eight priorities presented. They were also asked to compare and rate their agreement level on a 5-point Likert scale with four funding scenarios described. The frequency of each priority, independent of where it was ranked in relation to the other priorities, was calculated. Regression analyses were conducted to measure the association between respondents' demographics and selected priorities with their agreement level for each funding scenario. RESULTS: Among the survey respondents, Improved Quality of Life and Effective Health Care were most frequently selected as top priorities. Also, 79.2% of respondents agreed with equal access to DRDs across Canada, and 73.0% agreed with DRD funding if additional expenses are justified in the DRD's cost-effectiveness. Approximately half agreed to pay for DRDs independent of their effectiveness. There were no geographic differences in priorities. Selecting Effective Health Care in the top priorities was positively associated with both prioritizing other programs over programs for rare diseases and DRD funding only if deemed as cost-effective. Respondents, who selected National Access as one of the top priorities, were less likely to agree to fund DRDs only if deemed as cost-effective and were more likely to agree with the scenario to provide national access to DRDs. CONCLUSIONS: The survey results suggest the level of public support for funding decisions and programs that incorporate assessment of the effectiveness of drugs for improving quality of life, and to promote similar access across Canada. The responses anticipate public responses to different policy scenarios and the priorities that underlie them. Decision-makers may find it useful to consider whether and how to incorporate these results into policy decisions and their justification to citizens and patients.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".