Bibliographic record
Abstract
Chronic diseases (including cardiovascular diseases, cancers, chronic respiratory diseases, musculoskeletal conditions and diabetes mellitus) and their biomedical risk factors (such as obesity, hypertension and hyperlipidaemia) are a serious and urgent global population health problem.1 In Australia, chronic diseases are responsible for eight out of every 10 premature deaths,2 over 11 million of the population have at least one chronic disease and chronic diseases account for 80% of years lost due to ill health, disability or early death.3 The financial burden of chronic diseases on the Australian community is considerable and growing. Based on 2008/2009 data, the Australian Institute of Health and Welfare (AIHW) estimates that 36% of all health spending—about $27 billion a year3—is spent on treating chronic diseases, with this amount dwarfed when accounting for the costs of lost productivity and caring for people with disability.3 Chronic diseases also come at a considerable personal cost to individuals and their families, and adversely affect how millions of Australians live their lives every day.3 Promisingly, it is widely acknowledged that much of the burden of chronic disease is preventable. The AIHW estimates that at least 31% of the burden of disease could be prevented by reducing exposure to modifiable risk factors such as tobacco use, harmful alcohol use, high body mass, physical inactivity and high blood pressure.4 Yet, despite recognition of the urgent need to control chronic diseases5 and growing evidence on both the effectiveness and cost-effectiveness of prevention,6 and the significant successes in some countries in the prevention of cardiovascular disease, no country, including Australia, has successfully reversed or even contained the rising overall burden of chronic disease. Australia currently spends more than $2 billion on preventive health each year, or around $89 per person—significantly less than other comparable OECD countries.3 The argument is often made that Australia should increase spending on preventive health. However, research conducted by The Australian Prevention Partnership Centre7 has recommended that rather than focusing on “how much” we spend, we should focus on “where we target” the spending. In this research, commissioned by the Foundation for Alcohol Research and Education (FARE), Professor Alan Shiell and Hannah Jackson reviewed what was known about how much Australia spends on disease prevention each year and how this compares with other countries such as Canada, New Zealand and the USA. A summary of the report's findings is in this journal (see page 7 of this issue). In brief, the main conclusion of this research was that comparing our current spending on prevention with that of other countries tells us nothing about how much we ought to spend, because it does not cover whether increases in spending would be efficient or equitable. Instead, it would be more useful if we were to focus on the cost-effectiveness of interventions rather than the total amount spent. In other words, the key to determining the best way to finance prevention is to reorganise the current suite of preventive health activities and increase spending in those activities assessed as most cost-effective. To gain a sense of where new resources could be targeted most effectively, The Australian Prevention Partnership Centre funded and commissioned this special issue of the Health Promotion Journal of Australia. The following commentaries comprise a thought experiment asking what would happen if spending were to be increased by just 5% of the current annual budget, or about $100 million per year. The commentators are Australian and international leaders in preventive health across academia, advocacy and policy, who were invited to answer the question: “If you had $100 million a year to spend on prevention, what would you spend it on to make the most impact?”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".