Bibliographic record
Abstract
This commentary provides a practical perspective on the benefits of regionalization within the context of the lead paper by Lewis and Kouri. Discussion of the best structure of health systems needs to take into account the reality that constitutional and funding responsibility--and therefore accountability--for our healthcare system lies with provincial governments. Accountability at the provincial government level is simply a part of regionalization and should be expected by health system managers. Regionalization of healthcare is in its infancy. Systems necessary to support regionalization, which can only be established under a regionalized structure (e.g., information technology, procurement, human resources and service planning), are evolving. Given the size and complexity of the healthcare system, as we gain more experience with regionalization the systems we need to support it will attain their potential, as will the managers of the system. Regionalization was the most appropriate option for healthcare in the 1990s given the objectives of provincial governments to achieve efficiencies, better utilize resources and improve patient care. We must allow regionalization to evolve in order to see long-term improvements in the healthcare system.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.074 | 0.057 |
| Insufficient payload (model declined to judge) | 0.008 | 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".