Bibliographic record
Abstract
Lewis and Kouri's analysis of the Canadian experience of regionalization resonates strongly with the Australian experience, but Australia lacks a clear trend. Rather, there is a record of oscillation in the system between consolidation "at state level" and devolution "to regions or networks", although there is a general movement away from atomization "that is, stand-alone hospitals and health services". This paper briefly reviews the Australian situation, and then focuses on the differences in our two countries' regionalization experiences. I argue that two Australian problems "the federal/state split and the struggle for control" have led to different outcomes, and they throw a different light on the basic question posed by Lewis and Kouri: How might regionalization better contribute to health system goals? This paper is written from the perspective of a participant in Australia's regionalization process--most recently as chair of the governance and funding task group of a comprehensive health system review in South Australia, one of Australia's eight states and territories. The task group recommendation to regionalize the system "Generational Health Review 2003" was accepted, and the changes come into force in July 2004. This looks like being the last devolution in the Australian system for some time.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.021 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.027 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".