Bibliographic record
Abstract
Perception often is confused with fact. On each side of the CanadianeUS border Canadians and Americans traditionally have held that each other’s own style of health care provision is better than the other, just needing a few tweaks to get it perfect. Of late, the reality faced by both countries is that business as usual cannot be sustained, and that changes will be introduced either through research and discussion, or will be thrust upon us as the money runs out. There has been a noticeable upsurge in the interest of many US physicians in the Canadian model, fueled in part by the looming apprehension that the US system needs improvement. The alarming increase in the American debt as well as the other stressors on the health care system of which we are so well aware (aging population, expensive technologies, more informed and demanding population, inadequate care for the working poor, and so forth) warns of a brick wall up ahead. In addition, Michael Moore’s recent movie Sicko has shocked those who care about the system by showing half truths: that the American system is callous, inefficient, profit driven, and unfair, while other systems including those of Canada, Britain, and France are sanctuaries of compassion, accessibility, affordability, and serene satisfaction for its citizens. It is not within my knowledge or prerogative to comment on the limitations of the American health care system, except to acknowledge what we all knowdthat some of the finest medical institutions of higher learning and care in the world reside there. Nor am I in a position to speak with understanding of the systems in Great Britain or France. I have practiced radiology for 28 years in Canada in 4 provinces, and before that was trained in Canadian medical schools and residency programs. I have been a patient within the Canadian system, and have ongoing interactions with the system at this level for myself and my family. Our system is good, but far from perfect. To discuss the Canadian System, I
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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.061 | 0.033 |
| Scholarly communication | 0.030 | 0.010 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.037 | 0.039 |
| Insufficient payload (model declined to judge) | 0.012 | 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".