Bibliographic record
Abstract
Canadians spend more on healthcare than people in most other countries. We are fifth in the OECD in terms of health spending per capita, and eighth out of 28 countries in terms of health spending as a percentage of GDP. Given these facts, it is appropriate to discuss the issue of value for money in healthcare. In their paper, McGrail et al. present four challenges to improving value for money in Canadian healthcare: a lack of analysis of the hospital sector; the need to learn from rate variation analysis; the slow uptake of the electronic health record (EHR); and the need to measure health outcomes. Our paper addresses each of these points, but also proposes that a broader outlook is needed to come to grips with this question. It is essential to go beyond supply-side cost control, and also take into account the needs of the patient. Moreover, we need to look beyond our borders to learn how other countries have been able to evolve universal publicly funded health systems without long waiting times.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.067 | 0.061 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".