Pharmacare in Canada: The paediatric perspective
Bibliographic record
Abstract
Canada's drug insurance system is one of the most expensive in the world, yet millions of Canadians still struggle to access necessary medications. As a result, provincial, territorial, and federal governments are considering public pharmacare policy proposals to ensure that all Canadians can access the medications they need. Pharmacare policies offer an opportunity to prioritize children and youth, whose unique drug needs have long been neglected. Prescription drug use is common in this population, with approximately half of Canadian children and youth requiring at least one prescription in any given year. Drug use remains concentrated, however, among those with complex, chronic, and serious diseases. Children and youth rely heavily on compounded and off-label prescription drugs, which impacts safety, efficacy, palatability, and cost. Reimbursement decision-making bodies do not appropriately value the unique benefits of paediatric drugs, including child-friendly formulations, improved quality of life for children and families, and cost-savings outside the healthcare system. Regardless of the pharmacare model ultimately implemented, ensuring universal, comprehensive, and portable prescription drug coverage for all children and youth is essential. To accomplish this, paediatric drug experts should develop a national, evidence-informed formulary of paediatric drugs. Health Canada should also improve processes to make commercial paediatric drugs and child-friendly formulations more available and accessible. The federal government must also support paediatric drug research and development to this end.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".