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Record W2792522841 · doi:10.1017/s1744133117000421

Expanding the breadth of Medicare: learning from Australia

2018· article· en· W2792522841 on OpenAlexaboutno aff
Stephen Duckett

Bibliographic record

VenueHealth Economics Policy and Law · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentHealth careBusinessMedicineEconomic growthFinanceEconomics

Abstract

fetched live from OpenAlex

The design of Australia's Medicare programme was based on the Canadian scheme, adapted somewhat to take account of differences in the constitutional division of powers in the two countries and differences in history. The key elements are very similar: access to hospital services without charge being the core similarity, universal coverage for necessary medical services, albeit with a variable co-payment in Australia, the other. But there are significant differences between the two countries in health programmes - whether or not they are labelled as 'Medicare'. This paper discusses four areas where Canada could potentially learn from Australia in a positive way. First, Australia has had a national Pharmaceutical Benefits Scheme for almost 70 years. Second, there have been hesitant extensions to Australia's Medicare to address the increasing prevalence of people with chronic conditions - extensions which include some payments for allied health professionals, 'care coordination' payments, and exploration of 'health care homes'. Third, Australia has a much more extensive system of support for older people to live in their homes or to move into supported residential care. Fourth, Australia has gone further in driving efficiency in the hospital sector than has Canada. Finally, the paper examines aspects of the Australian health care system that Canada should avoid, including the very high level of out-of-pocket costs, and the role of private acute inpatient provision.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.864
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.116
GPT teacher head0.345
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations56
Published2018
Admission routes1
Has abstractyes

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