Reforming Primary Care in Australia: A Narrative Review of the Evidence from Five Comparator Countries
Bibliographic record
Abstract
The need for reform of primary care is driven by health system inequity, inefficiency, sub-optimal quality of care and outcomes. In Australia, there has been no systematic analysis of the relevance and applicability of international reforms of differing models of primary care delivery and the implications for addressing these issues in the local context. We used a narrative review and synthesis approach to analyse evidence from four English-speaking comparator countries (New Zealand, Canada, United Kingdom, United States of America) and one European country (Netherlands). In this review the term "primary care" refers to the system of health care workers (predominantly general practice, nursing and allied health professionals) who provide locally-based first contact care in the community setting. The existing international evidence does not support the adoption of any specific model of primary care delivery that is suitable to the Australian context. However, the evidence does suggest four key mechanisms that should form the basis of future reform. This includes the funding of GP services, quality and performance frameworks, stronger regional structures to support primary care, and investment in practice infrastructure. This paper provides an overview of the review methods and findings. A full report and in-depth discussion of findings are available from http://www.anu.edu.au/aphcri/Domain/PHCModels/index.php
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".