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Physician assistants in Australia

2014· article· en· W2329098029 on OpenAlexaboutno aff
Richard Murray, Deborah A. O'Kane

Bibliographic record

VenueJAAPA · 2014
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforcePhysician supplyConstraint (computer-aided design)Context (archaeology)SkepticismPopulationBequestNursingPublic relationsPhysician assistantsPolitical scienceMedicineHealth careBusinessNurse practitionersLawGeographyEngineering

Abstract

fetched live from OpenAlex

[Extract] Physician assistants (PAs) are making inroads in Australia. Medical extension is an idea whose time has finally come. As the familiar demand-side triad of population aging, wants, and technology collides with workforce and financial constraint, change for Australia will be a necessity, not an option. With 3.3 physicians for every 1,000 of its population, Australia actually has quite a reasonable supply of doctors. In comparison, the United States has 2.5 per 1,000, Canada 2.4, and the United Kingdom 2.8.1 The real reasons for the equally real shortages of physicians are the problems of urban concentration, the imbalance between clinical generalists and subspecialists, and tasks being performed by physicians that might be more efficiently done by others.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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.

Opus teacher head0.091
GPT teacher head0.478
Teacher spread0.387 · 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.

Study designObservational
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

Citations2
Published2014
Admission routes1
Has abstractyes

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