How do government health departments in Australia access health economics advice to inform decisions for health? A survey
Bibliographic record
Abstract
BACKGROUND: Government anticipates that health economic analysis will contribute to evidence-based policy development. Early examples in Australia where this expectation has been met include the economic evaluations of breast and cervical screening. However, the level of integration of health economics within health services that require this advice appears uneven. We sought to describe how government health departments in Australia use specialist health economic advice to inform policy and planning and the mechanisms through which they access this advice. METHODS: Information describing the arrangements for gaining health economics input into health decision-making was sought through interviews with a purposeful sample of economists and non-economists employed by all departments of health in Australia (state, territories and national). The survey was undertaken in August 2004. To aid interpretation of the results eight health economic functions were identified. As a comparison, four other government departments in NSW provided information about their access to economic advice. RESULTS: All health departments except one reported being current users of health economics expertise. A variety of arrangements were described to source this, from building organisational capacity with self-sufficient in-house units to forging links with external sources. However, specialist positions for economists or health economists employed within health were few. A framework mapping these arrangements for sourcing advice with the eight common health economic functions to be met is presented. All other non-health government departments approached accessed economic advice, with three having in-house units. DISCUSSION: A small health economics capacity in Australia has been established over the past 30 years through a variety of structural and strategic mechanisms. Health departments value health economic advice and use a variety of arrangements to obtain this. These arrangements have strengths and weaknesses depending upon the task to be undertaken. The lack of uniformity of approach suggests that health departments are still seeking the best ways to incorporate this form of specialist advice into mainstream decision-making. IMPLICATIONS: Summarises ways that governments source specialist services. Demonstrates how to describe an organisation's need for specialist services as a set of functions. This approach could be applied to assessing need for other specialist areas of advice.
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 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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".