Provincial Elections and Timing of Cancer Drug Funding
Bibliographic record
Abstract
BACKGROUND: Concerns have been raised about the potential influence of political pressures on drug funding decisions. We evaluated the temporal relationship between cancer drug funding and provincial elections in 9 Canadian provinces. METHODS: New indications for cancer drugs between January 2003 and December 2012 were identified, and the dates of official provincial funding dates and election dates between 1 January 2003 and 31 December 2014 were retrieved. The probability of drug funding announcements in the 60-day period preceding a provincial election was evaluated using binomial probability distribution analysis. RESULTS: Data from 9 provinces (all Canadian provinces except Quebec) were available. During the period of interest, 69 new indications for 39 individual drugs were identified. Variation in the availability of funding dates was identified. At the time of data collection, 2 provinces did not have data available for all 69 indications. For the 9 provinces, the number of funded indications during the 60-day period preceding an election ranged from 0 to 3; however, no differences in the proportion of indications funded pre-election were identified. Additional analyses also failed to demonstrate any significant associations with the 90-day period before an election, or the 60- and 90-day periods after an election. CONCLUSIONS: We observed no clear temporal relationship between provincial election dates and funding decisions in this recent Canadian sample of new indications 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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".