Bibliographic record
Abstract
Cross-border pharmacy sales of prescription drugs to U.S. patients by Canadian Internet pharmacies have generated significant controversy in the U.S. and Canada. Violative of U.S. legislation and Canadian professional codes of conduct, cross-border pharmacy has nonetheless flourished in response to strong demand and incomplete enforcement. Proponents laud the greater affordability of needed drugs provided by cross-border pharmacy; opponents decry the practice as unsafe, economically ill-advised and harmful to Canadian interests in the long term. This article evaluates the safety arguments that have been put forward by the U.S. Food and Drug Administration and others and concludes that they do not justify a prohibition on prescription drug imports from Canada. Similarly. Canadian professional regulatory bodies' objections to the participation of Canadian pharmacists and physicians in cross-border dispensing are a misapplication of conduct rules developed in another context. Objections to cross-border drug sates based on an economic analysis assume normative positions that should be explicitly identified and socially determined. On the other hand, if patient safety, professional responsibility and economic arguments fail to provide adequate support for a policy of prohibition, then self-interest in protecting domestic drug supplies and prices may support, at least from a Canadian perspective, some constraints on cross-border pharmacy.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 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".