Understanding the Reasons for Provincial Discordance in Cancer drug Funding—A Survey of Policymakers
Bibliographic record
Abstract
Background: Cancer drug-funding decisions between provinces shows discordance. The pan-Canadian Oncology Drug Review (pcodr) was implemented in 2011 partly to address uneven drug coverage and lack of transparency in the various provincial cancer drug review processes in Canada. We evaluated the underlying reasons for ongoing provincial discordance since the implementation of pcodr. Methods: Participation in an online survey was solicited from participating provincial ministries of health (mohs) and cancer agencies (cas). The 4-question survey (with both multiple-choice and free-text responses) was administered between 4 March 2015 and 1 April 2015, inclusive. Anonymity was ensured. Descriptive statistics were used to evaluate responses. Results: Data were available from 9 provinces (all Canadian provinces except Quebec), with a response rate of 100%. The 12 responses received each came from a senior policymaker with more than 5 years' experience in cancer drug funding decision-making (5 from mohs, 7 from cas). Responses for 3 provinces came from both a moh representative and a ca representative. The most common reason for funding a drug not recommended by pcodr was political pressure (64%). The most common reason not to fund a drug recommended by pcodr was budget constraints (91%). The most common reason for a province to fund a drug before completion of the pcodr review was also political pressure (57%). Conclusions: Political pressure and budgetary constraints continue to affect equity of access to cancer drugs for patients throughout Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".