Bibliographic record
Abstract
Most people benefit from healthcare. However, Canadians remain dissatisfied because too often we receive faulty care and delayed care, not supported by evidence. Browman and colleagues relate successful efforts to introduce evidence-based care. They show that strong champions can be effective even in insane environments. The collaborative approach suggested is moving and thoughtful. Sharing between knowledge and financial stewards, including the use of stories, is especially valuable when financial stewardshipis not possible because we lack information about the local outcomes of care. Goodwill between stewards is especially necessary when there are few external incentives to provide excellent care. In healthcare good deeds are punished, not rewarded. Canadian governments fail to regulate healthcare because of the conflict of interest arising when the same group not only regulates care but also functions as insurer, governor, administrator and evaluator. We need radical change to eliminate the perverse incentives and bizarre management practices that bedevil our healthcare system and impede the use of evidence. Fundamental changes in organization and evaluation proposed by the Halifax Chamber of Commerce and the Kirby and Mazankowski committees will help. Separating the functions of insurer, administrator, evaluator and regulator is ethical and necessary.
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 | 0.011 | 0.038 |
| 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 | 0.016 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.060 | 0.044 |
| Insufficient payload (model declined to judge) | 0.011 | 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".