Bibliographic record
Abstract
Australia has an evolving national cancer control agenda. In this paper, we review the history and development of cancer control policies in Australia up to the end of 2005, and discuss the principal publications produced by both government and non-government groups which have given rise to cancer control recommendations, goals and targets. These cancer control plans have arisen in response to the impact of cancer on the Australian community and in recognition of the health gains that can be made through effective cancer control. They have been developed either in the context of a broader framework of health policy or as specific endeavours in regard to cancer. The specific recommendations and strategies proposed have focused on reducing the impact of cancer in the Australian population. Most commonly, recommendations, goals, and targets within the cancer control plans have addressed points along the continuum of care, specific cancers, and frameworks and processes. The strength of these reports is their comprehensive approach in identifying priority cancers and areas where specific developments should impact on morbidity and mortality. In the future, cancer control plans should be better supported by economic evaluations, and greater financial support for implementation and regular assessment is needed to identify progress on cancer outcomes. The more recent national and State cancer control plans include the development of frameworks to foster a coordinated and cohesive approach to the delivery of cancer care. These plans represent important reforms in cancer care in Australia, and have the potential to reduce the impact of cancer on the community and improve health outcomes.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".