House of Commons' Standing Committee on Health: Development of a National Pharmacare Program
Bibliographic record
Abstract
Canada should implement national pharmacare consistent with the principles outlined in the Pharmacare 2020 report Morgan et al 2015a The best evidence we have shows that national pharmacare will save approximately 7 billion and more importantly hundreds of lives each year Morgan et al 2015bThe issue then is not whether to institute national pharmacare but how For even though the need for national pharmacare has been plain since the 1964 Hall Commission the landscape of medicine and pharmaceuticals has changed dramatically since then Of particular note is the pharmaceutical industry's growing interest in drugs that target relatively small patient populations often described interchangeably as 'orphan' 'niche' or 'specialty' drugs in the pursuit of socalled 'personalized' or 'precision medicine' This brief focuses specifically on the challenges posed by the push for more personalized medicine These challenges serve to underscore why national pharmacare is needed and define some of its essential features
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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 | 0.045 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.016 | 0.005 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.037 | 0.028 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".