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Extending rural and remote medicine with a new type of health worker: Physician assistants

2007· review· en· W2060966228 on OpenAlexaboutno aff
Teresa O’Connor, Roderick S. Hooker

Bibliographic record

VenueAustralian Journal of Rural Health · 2007
Typereview
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforceEconomic shortagePhysician assistantsMedicineWorkforce planningHealth careNursingRural healthRural areaService (business)BusinessFamily medicineEconomic growthMarketingNurse practitionersGovernment (linguistics)

Abstract

fetched live from OpenAlex

The purpose of this paper was to demonstrate that the medical workforce shortage is an international phenomenon and to review one of the strategies developed in the USA in the late 1960s: the physician assistant model of health service provision. The authors consider whether this model could provide one strategy to help address the medical workforce shortage in Australia. A systematic review of the literature about medical workforce shortages, strategies used to address the medical workforce shortage, and the physician assistant role was undertaken. Literature used for the review covered the period 1967-2006. Physician assistants provide safe, high-quality and cost-effective primary care services under the direction of a doctor and respond to workforce shortages in rural and remote areas, family practice medicine and hospital settings. This model of health care provision has been adopted in several other developed countries, including England, Scotland, the Netherlands and Canada. The physician assistant concept might provide Australia with a novel strategy for addressing its medical workforce shortage, particularly in rural and remote settings.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.228
GPT teacher head0.546
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations32
Published2007
Admission routes1
Has abstractyes

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