Bibliographic record
Abstract
Scientists have written numerous papers studying different aspects of health systems in the world. Comparing health systems in the world is essential for policymakers to learn from each other to make healthcare services effective with better outcomes and decrease the cost of healthcare services. In the world, countries have different health systems. The difference in the health systems is a combination of components that are specific to each country based on the financial status of healthcare, workforce, and infrastructures. This paper will evaluate the contrast of Canadian and American health systems payment systems, timely access, and healthcare quality outcomes. Both countries are well-developed countries that have a health system with excellent infrastructure and effective healthcare services. However, the system operates differently in both countries. America does not have a universal healthcare plan and spends more money per capita compared to Canada. The United States has a lower rank than its peer, underperforms in maternal mortality, infant mortality, preventable deaths, and life expectancy. On the other hand, Canada has a universal healthcare plan for all Canadian residents and performs better in life expectancy, infant mortality, and maternal mortality. However, waiting for specialized care is longer than in the United States.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.006 |
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".