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Record W2988935879 · doi:10.31128/ajgp-05-19-4929

Remodelling general practice training: Tension and innovation

2019· article· en· W2988935879 on OpenAlexaff
James B. Brown, Catherine Kirby, Susan Wearne, David Snadden

Bibliographic record

VenueAustralian Journal of General Practice · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Workforce Issues
Canadian institutionsUniversity of Northern British ColumbiaUniversity of British Columbia
Fundersnot available
KeywordsTraining (meteorology)DialecticStakeholderOrganisational changeGeneral practiceQualitative researchManagementPolitical sciencePublic relationsMedical educationEngineering ethicsSociologyMedicineEngineeringSocial scienceGeography

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The transfer of general practice training in Australia to the two general practice colleges is an opportunity for change in the model of training. The dialectical theory of institutional change suggests that change occurs where organisational structures of training are in tension with the needs of those delivering training, and effective change arises from innovation within these tension points. These tensions have also been faced by general practice training organisations internationally, where solutions have also been crafted. By exploring training tensions and responses to these, the aim of this study was to inform the remodelling of general practice training in Australia. METHOD: Senior educators and stakeholder representatives in Australia and internationally were interviewed to identify tensions in training delivery and innovative responses to these. An interpretative qualitative analysis was undertaken. RESULTS: Eight key tensions and associated innovative responses were identified. DISCUSSION: Drawing from the findings, this article provides recommendations for remodelling general practice training in Australia.

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)0.005
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.002
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 head0.148
GPT teacher head0.479
Teacher spread0.331 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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