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Record W7128624526 · doi:10.26180/4969367.v1

The regulation of medical practice in Australia, Canada, United States and Britain

2017· article· W7128624526 on OpenAlexaboutno aff
Bob Birrell

Bibliographic record

VenueMonash University · 2017
Typearticle
Language
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCommonwealthGovernment (linguistics)Medical practiceState (computer science)Subject (documents)Clinical Practice

Abstract

fetched live from OpenAlex

Overseas trained doctors (OTDs) are playing an important role as medical officers and specialists in the Australian public hospital system and as general practitioners in ‘area of need’ locations. This role is increasing as a result of the recruiting initiatives flowing from the Commonwealth Government's Strengthening Medicare program. Yet there are no requirements in Australia that these OTDs be first subject to a formal assessment of their medical knowledge, clinical skills and practice performance in a supervised hospital setting. A review of the situation in Canada, the United States and Britain shows that OTDs wishing to practise in these countries first have to undergo such an assessment. The reasons why Australia is different are explored. It is concluded that State and Commonwealth Government concerns about the supply of doctors have overridden worries within the medical profession about the readiness of OTDs to practice in Australia without formal assessment and further training. Copyright. Monash University and the author/s

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.518

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0090.012
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.381
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes1
Has abstractyes

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