Evaluating health policy capacity: Learning from international and Australian experience
Bibliographic record
Abstract
BACKGROUND: The health sector in Australia faces major challenges that include an ageing population, spiralling health care costs, continuing poor Aboriginal health, and emerging threats to public health. At the same time, the environment for policy-making is becoming increasingly complex. In this context, strong policy capacity - broadly understood as the capacity of government to make "intelligent choices" between policy options - is essential if governments and societies are to address the continuing and emerging problems effectively. RESULTS: This paper explores the question: "What are the factors that contribute to policy capacity in the health sector?" In the absence of health sector-specific research on this topic, a review of Australian and international public sector policy capacity research was undertaken. Studies from the United Kingdom, Canada, New Zealand and Australia were analysed to identify common themes in the research findings. This paper discusses these policy capacity studies in relation to context, models and methods for policy capacity research, elements of policy capacity and recommendations for building capacity. CONCLUSION: Based on this analysis, the paper discusses the organisational and individual factors that are likely to contribute to health policy capacity, highlights the need for further research in the health sector and points to some of the conceptual and methodological issues that need to be taken into consideration in such research.
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 | 0.113 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".