Bibliographic record
Abstract
Overview Ineffective management of heart disease, asthma, diabetes and other chronic diseases costs the Australian health system more than $320 million each year in avoidable hospital admissions. At best, our primary care system provides only half the recommended care for many chronic conditions. Only a quarter of the nearly one million Australians diagnosed with type 2 diabetes get the monitoring and treatment recommended for their condition. Each year there are more than a quarter of a million admissions to hospital for health problems that potentially could have been prevented. Yet each year the government spends at least $1 billion on planning, coordinating and reviewing chronic disease management and encouraging good practice in primary care. Three quarters of Australians over the age of 65 have at least one chronic condition that puts them at risk of serious complications and premature death. Social, economic and environmental changes are the best way to prevent these diseases. But there are much better outcomes where good quality primary care services are in place. Our primary care system is not working anywhere near as well as it should because the way we pay for and organise services goes against what we know works. The role of GPs is vital, but the focus must move away from fee-for-service payments for one-off visits. A broader payment for integrated treatment would help to focus care on patients and long-term outcomes. Primary Health Networks should be given more responsibility for local primary care services. The evidence shows that a consistent, coordinated approach to specific diseases helps primary care more effectively prevent and manage chronic conditions. In regional areas, clear targets and well-designed incentives for disease prevention are vital. Simple reforms can reduce the burden on Australian hospitals, and make patients healthier for longer.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 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".