Bibliographic record
Abstract
In an era of increasingly complex medical care and escalating costs, healthcare decision-makers often rely on a broad range of indicators to gauge the health of a population, the quality of hospital care and the performance of healthcare systems. Reports that rank the health of Canadians and Canada's healthcare systems according to these indicators are widely cited in the media. These reports attempt to condense a complicated array of statistics into a relatively simple number, a rank that is used to make international and provincial comparisons. These reports have often been inconsistent. Unlike a familiar economic indicator - the gross domestic product (GDP), which represents a complex entity with a single number calculated according to an internationally agreed-upon methodology - rankings of health and healthcare are not yet standardized or well understood. This article aims to improve readers' understanding of ranking reports. It outlines the components and processes that underlie health rankings and explores why such rankings can be difficult to interpret.
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 | 0.148 | 0.398 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.042 | 0.059 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".