Bibliographic record
Abstract
In 2017 the healthcare system in the United States found itself in a subacute crisis, as Republican lawmakers tried without success to forge a consensus around repealing and replacing the Affordable Care Act.Canada, meanwhile, remains the frozen north of healthcare policy-a system not so much in crisis, but in a chronically uncomfortable stasis.This obstipation is so extreme that it has already prompted publication of a detailed study evocatively entitled Paradigm Freeze: Why It Is So Hard to Reform Health Care in Canada (Lazar et al. 2013).In recent years, a group of scholars at Queen's University have taken this unhappy situation as a positive challenge.They organized four conferences, held annually from 2013 to 2016, aptly entitled the Queen's Health Policy Change Conference Series, and those conference proceedings in turn have led to three collections of essays-the last of which is in your hands or on your digital screen at the moment.All four conferences and the reports and publications related to them have focused carefully not just on strategies that would advance Canadian health policy, but how in concrete terms those policies might be implemented.It is true that the essays in the three volumes vary in the specificity of their action plans, and in their degree of optimism about whether policy makers in Canada's notoriously dysfunctional federation will ever converge around an agenda of major reforms to the nation's healthcare systems.But what they all reflect is the wide consensus that the modes of organizing and financing Canadian healthcare are outmoded, and that, despite some of the finest healthcare professionals and managers in the xviii C.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".