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Record W313469735

Increasing medical student interest in general practice in New Zealand: where to from here?

2010· article· en· W313469735 on OpenAlexaboutno aff
Phillippa Poole, David Bourke, Boaz Shulruf

Bibliographic record

VenuePubMed · 2010
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGeneral practiceFlexibility (engineering)General interestQuarter (Canadian coin)Special Interest GroupMedical educationFamily medicineManagement
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: To meet increasing health demands, increasing the proportion of local graduates entering general practice is imperative. METHODS: Students entering or exiting The University of Auckland's medical programme from 2006 to 2008 were invited to complete a tracking project survey. Levels of interest in general practice were determined along with characteristics associated with a greater or lesser interest in this career. RESULTS: 712 students replied--a response rate of 80%. At entry, 40% of students had a strong interest in a career in general practice, and at exit, 29% (P =0.003). A quarter at each time point had no interest. The proportion of domestic students born outside NZ or Australia was 160/376 (42.5%). There were significantly higher levels of interest in general practice among females, students born in NZ, and those from outside Auckland--especially rural origin. Flexibility in career was more important to students with a strong interest in general practice than those with no interest. DISCUSSION: Auckland medical students have levels of interest in general practice comparable with international data. Increasing this interest further may require admission of a greater proportion of students from those groups with higher interest levels, greater emphasis on the positive aspects of general practice, and on GPs as equals to other specialists.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
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 science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.439
Teacher spread0.383 · 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 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

Citations16
Published2010
Admission routes1
Has abstractyes

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