Comprehensive primary health care under neo-liberalism in Australia
Bibliographic record
Abstract
This paper applies a critical analysis of the impact of neo-liberal driven management reform to examine changes in Australian primary health care (PHC) services over five years. The implementation of comprehensive approaches to primary health care (PHC) in seven services: five state-managed and two non-government organisations (NGOs) was tracked from 2009 to 2014. Two questions are addressed: 1) How did the ability of Australian PHC services to implement comprehensive PHC change over the period 2009-2014? 2) To what extent is the ability of the PHC services to implement comprehensive PHC shaped by neo-liberal health sector reform processes? The study reports on detailed tracking and observations of the changes and in-depth interviews with 63 health service managers and practitioners, and regional and central health executives. The documented changes were: in the state-managed services (although not the NGOs) less comprehensive service coverage and more focus on clinical services and integration with hospitals and much less development activity including community development, advocacy, intersectoral collaboration and attention to the social determinants. These changes were found to be associated with practices typical of neo-liberal health sector reform: considerable uncertainty, more directive managerial control, budget reductions and competitive tendering and an emphasis on outputs rather than health outcomes. We conclude that a focus on clinical service provision, while highly compatible with neo-liberal reforms, will not on its own produce the shifts in population disease patterns that would be required to reduce demand for health services and promote health. Comprehensive PHC is much better suited to that task.
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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".