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Record W2181599922 · doi:10.1071/hc10273

Review of registration requirements for new part-time doctors in New Zealand, Australia, the United Kingdom, Ireland and Canada

2010· article· en· W2181599922 on OpenAlexaboutno aff
Sharon Leitch, Susan Dovey

Bibliographic record

VenueJournal of Primary Health Care · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsKingdomNew Zealand studiesPolitical scienceNorthern irelandMedicineOptometryFamily medicineHistoryEthnologySociologySocial science

Abstract

fetched live from OpenAlex

INTRODUCTION: By the time medical students graduate many wish to work part-time while accommodating other lifestyle interests. AIM: To review flexibility of medical registration requirements for provisional registrants in New Zealand, Australia, the United Kingdom, Ireland and Canada. METHODS: Internet-based review of registration bodies of each country, and each state or province in Australia and Canada, supplemented by emails and phone calls seeking clarification of missing or obscure information. RESULTS: Data from 20 regions were examined. Many similarities were found between study countries in their approaches to the registration of new doctors, although there are some regional differences. Most regions (65%) have a provisional registration period of one year. Extending this period was possible in 91% of regions. Part-time options were possible in 75% of regions. All regions required trainees to work in approved practice settings. DISCUSSION: Only the UK provided comprehensive documentation of their requirements in an accessible format and clearly explaining the options for part-time work. Australia appeared to be more flexible than other countries with respect to part- and full-time work requirements. All countries need to examine their registration requirements to introduce more flexibility wherever possible, as a strategy for addressing workforce shortages.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.453
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.083
GPT teacher head0.437
Teacher spread0.353 · 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 designNot applicable
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

Citations4
Published2010
Admission routes1
Has abstractyes

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